Single-line cross component linear model prediction mode

ABSTRACT

Devices, systems and methods for digital video coding, which includes cross-component prediction, are described. In a representative aspect, a method for video coding includes receiving a bitstream representation of a current block of video data comprising a luma component and a chroma component, determining parameters of a linear model based on a first set of samples that are generated by down-sampling a second set of samples of the luma component, and processing, based on the parameters of the linear model, the bitstream representation to generate the current block.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.17/115,388, filed on Dec. 8, 2020, which is a continuation ofInternational Application No. PCT/IB2019/057699, filed on Sep. 12, 2019,which claims the priority to and benefits of International PatentApplication No. PCT/CN2018/105182, filed on Sep. 12, 2018,PCT/CN2018/108681, filed on Sep. 29, 2018, PCT/CN2019/088005, filed onMay 22, 2019. All the aforementioned patent applications are herebyincorporated by reference in their entireties.

TECHNICAL FIELD

This patent document relates to video coding and decoding techniques,devices and systems.

BACKGROUND

In spite of the advances in video compression, digital video stillaccounts for the largest bandwidth use on the internet and other digitalcommunication networks. As the number of connected user devices capableof receiving and displaying video increases, it is expected that thebandwidth demand for digital video usage will continue to grow.

SUMMARY

Devices, systems and methods related to digital video coding, andspecifically, low complexity implementations for the cross-componentlinear model (CCLM) prediction mode in video coding are described. Thedescribed methods may be applied to both the existing video codingstandards (e.g., High Efficiency Video Coding (HEVC)) and future videocoding standards (e.g., Versatile Video Coding (VVC)) or codecs.

In an example aspect, a method of video processing is disclosed. Themethod includes determining, for a conversion between a video and acoded representation of the video, at least using down-sampled lumasamples outside a luma block, a linear model used for predicting a firstchroma block of the video that is co-located with the luma block,determining, using down-sampled luma samples inside the luma block andthe linear model, predicted values of samples of the first chroma block,and performing the conversion based on the predicted values of samplesof the first chroma block; wherein the down-sampled luma samples outsidethe luma block are obtained by applying an outside filter to lumasamples outside the luma block; and wherein the down-sampled lumasamples inside the luma block are obtained by applying an inside filterto luma samples inside the luma block.

In another example aspect, a method of video processing is disclosed.The method includes determining, for a conversion between a video and acoded representation of the video, one or more cross-component linearmodels (CCLMs) used for prediction of a first component block from asecond component block during the conversion based on a rule that uses anumber of available neighboring samples or a size of the first componentblock; and performing the conversion using the cross-component linearmodel, wherein the cross-component linear model is one of: a CCLMderived from above and/or right-above neighboring values of the firstcomponent block (CCLM-A); a CCLM derived from left and/or left-belowneighboring values of the first component block (CCLM-T); or a CCLMderived from only left and top neighboring values of the first componentblock (CCLM-TL).

In another example aspect, a method of video processing is disclosed.The method includes generating downsampled outside luma samplescorresponding to chroma samples outside a chroma block of a video usinga first downsampling scheme; generating downsampled inside luma sampleswith corresponding to chroma samples inside the chroma block using asecond downsampling scheme; at least using the downsampled outside lumasamples to derive a linear model for cross-component prediction;determining predicted samples for the chroma block using the linearmodel and the downsampled inside luma samples; and performing aconversion between the video and a coded representation of the videousing the predicted samples for the chroma block.

In another example aspect, a method of video processing is disclosed.The method includes determining, for a conversion between a chroma blockof a video and a coded representation of the video, a linear model;generating prediction values of the chroma block from a luma block thatcorresponds to the chroma block based on the linear model; andperforming the conversion using the linear model; wherein the predictingthe chroma block from the luma block includes downsampling luma samplesabove the luma block by a first filtering method and dowsampling lumasamples to left of the luma block by a second filtering method, andwherein the linear model is determined at least based on the downsampledluma samples.

In another example aspect, a method of video processing is disclosed.The method includes determining, for a conversion between a video and abitstream representation of the video and based on a height (H) or awidth (W) of a chroma block of the video, parameters of a linear model,wherein the parameters of the linear model are based on downsampled lumasamples corresponding to chroma samples outside the chroma block, andwherein H and W are positive integers; generating a set of samples ofthe chroma block based on the parameters of the linear model and a setof downsampled luma samples inside a luma block corresponding to thechroma block; and performing, based on the generating, the conversionbetween the video and the bitstream representation.

In another example aspect, a method of video processing is disclosed.The method includes determining, for a conversion between a video and abitstream representation of the video and based on a height (H) or awidth (W) of a chroma block of the video, parameters of a linear model,wherein the parameters of the linear model are based on a first set ofluma samples that are generated by down-sampling a second set of lumasamples corresponding to chroma samples outside the chroma block, andwherein a number of the first set of luma samples is based on the heightor the width, and wherein H and W are positive integers; generating aset of samples of the chroma block based on the parameters of the linearmodel and a set of downsampled luma samples inside a luma blockcorresponding to the chroma block; and performing, based on thegenerating, the conversion between the video and the bitstreamrepresentation.

In one representative aspect, the disclosed technology may be used toprovide a method for cross-component prediction. This exemplary methodfor video coding includes receiving a bitstream representation of acurrent block of video data comprising a luma component and a chromacomponent, determining parameters of a linear model based on a first setof samples that are generated by down-sampling a second set of samplesof the luma component, and processing, based on the parameters of thelinear model, the bitstream representation to generate the currentblock.

In another representative aspect, a method of video coding is disclosed.The method includes using, during a conversion between a current blockof video and a bitstream representation of the current block, parametersof a linear model based on a first set of samples that are generated bydown-sampling a second set of samples of the luma component; andperforming, based on the parameters of the linear model, the conversionbetween the bitstream representation and the current block.

In another representative aspect, a method of video processing isdisclosed. The method includes performing a conversion between a currentvideo block and a bitstream representation of the current video blockusing a cross-component linear model in which multiple lines of lumasamples, at least one being non-adjacent to the current video block areused for the conversion using the cross-component linear model.

In yet another representative aspect, the above-described method isembodied in the form of processor-executable code and stored in acomputer-readable program medium.

In yet another representative aspect, a device that is configured oroperable to perform the above-described method is disclosed. The devicemay include a processor that is programmed to implement this method.

In yet another representative aspect, a video decoder apparatus mayimplement a method as described herein.

The above and other aspects and features of the disclosed technology aredescribed in greater detail in the drawings, the description and theclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example of locations of samples used for the derivationof the weights of the linear model used for cross-component prediction.

FIG. 2 shows an example of classifying neighboring samples into twogroups.

FIG. 3A shows an example of a chroma sample and its corresponding lumasamples.

FIG. 3B shows an example of down filtering for the cross-componentlinear model (CCLM) in the Joint Exploration Model (JEM).

FIG. 4 shows an exemplary arrangement of the four luma samplescorresponding to a single chroma sample.

FIGS. 5A and 5B shows an example of samples of a 4×4 chroma block withthe neighboring samples, and the corresponding luma samples.

FIGS. 6A-6J show examples of CCLM without luma sample down filtering.

FIGS. 7A-7D show examples of CCLM only requiring neighboring lumasamples used in normal intra-prediction.

FIGS. 8A and 8B show examples of a coding unit (CU) at a boundary of acoding tree unit (CTU).

FIG. 9 shows an example of 67 intra prediction modes.

FIGS. 10A and 10B show examples of reference samples for wide-angleintra prediction modes for non-square blocks.

FIG. 11 shows an example of a discontinuity when using wide-angle intraprediction.

FIGS. 12A-12D show examples of samples used by a position-dependentintra prediction combination (PDPC) method.

FIGS. 13A and 13B show examples of down-sampled luma sample positionsinside and outside the current block.

FIG. 14 shows a flowchart of yet another example method forcross-component prediction in accordance with the disclosed technology.

FIG. 15 is a block diagram of an example of a hardware platform forimplementing a visual media decoding or a visual media encodingtechnique described in the present document.

FIG. 16 shows an example of combining different luma down-samplingmethods together.

FIGS. 17A and 17B show examples of LM prediction with single-lineneighboring luma samples.

FIGS. 18A-18C show examples of LM prediction with neighboring lumasamples.

FIG. 19 is a flowchart for an example method of video processing.

FIG. 20 is a flowchart for an example method of video processing.

FIG. 21 is a flowchart for an example method of video processing.

FIG. 22 is a flowchart for an example method of video processing.

FIG. 23 is a flowchart for an example method of video processing.

FIG. 24 is a flowchart for an example method of video processing.

FIG. 25 is a block diagram of an example video processing system inwhich disclosed techniques may be implemented.

DETAILED DESCRIPTION

Due to the increasing demand of higher resolution video, video codingmethods and techniques are ubiquitous in modern technology. Video codecstypically include an electronic circuit or software that compresses ordecompresses digital video, and are continually being improved toprovide higher coding efficiency. A video codec converts uncompressedvideo to a compressed format or vice versa. There are complexrelationships between the video quality, the amount of data used torepresent the video (determined by the bit rate), the complexity of theencoding and decoding algorithms, sensitivity to data losses and errors,ease of editing, random access, and end-to-end delay (latency). Thecompressed format usually conforms to a standard video compressionspecification, e.g., the High Efficiency Video Coding (HEVC) standard(also known as H.265 or MPEG-H Part 2), the Versatile Video Coding (VVC)standard to be finalized, or other current and/or future video codingstandards.

Embodiments of the disclosed technology may be applied to existing videocoding standards (e.g., HEVC, H.265) and future standards to improveruntime performance. Section headings are used in the present documentto improve readability of the description and do not in any way limitthe discussion or the embodiments (and/or implementations) to therespective sections only.

1 EMBODIMENTS OF CROSS-COMPONENT PREDICTION

Cross-component prediction is a form of the chroma-to-luma predictionapproach that has a well-balanced trade-off between complexity andcompression efficiency improvement.

1.1 Examples of the Cross-Component Linear Model (CCLM)

In some embodiments, and to reduce the cross-component redundancy, across-component linear model (CCLM) prediction mode (also referred to asLM), is used in the JEM, for which the chroma samples are predictedbased on the reconstructed luma samples of the same CU by using a linearmodel as follows:

pred_(C)(i,j)=α·rec _(L)′(i,j)+β  (1)

Here, pred_(C)(i,j) represents the predicted chroma samples in a CU andrec_(L)′(i,j) represents the downsampled reconstructed luma samples ofthe same CU for color formats 4:2:0 or 4:2:2 while rec_(L)′(i,j)represents the reconstructed luma samples of the same CU for colorformat 4:4:4. CCLM parameters α and β are derived by minimizing theregression error between the neighboring reconstructed luma and chromasamples around the current block as follows:

$\begin{matrix}{\alpha = \frac{{N \cdot {\Sigma\left( {{L(n)} \cdot {C(n)}} \right)}} - {\Sigma\;{{L(n)} \cdot \Sigma}\;{C(n)}}}{{N \cdot {\Sigma\left( {{L(n)} \cdot {L(n)}} \right)}} - {\Sigma\;{{L(n)} \cdot \Sigma}\;{L(n)}}}} & (2) \\{{{and}\mspace{14mu}\beta} = {\frac{{\Sigma{C(n)}} - {\alpha \cdot {{\Sigma L}(n)}}}{N}.}} & (3)\end{matrix}$

Here, L(n) represents the down-sampled (for color formats 4:2:0 or4:2:2) or original (for color format 4:4:4) top and left neighboringreconstructed luma samples, C(n) represents the top and left neighboringreconstructed chroma samples, and value of N is equal to twice of theminimum of width and height of the current chroma coding block.

In some embodiments, and for a coding block with a square shape, theabove two equations are applied directly. In other embodiments, and fora non-square coding block, the neighboring samples of the longerboundary are first subsampled to have the same number of samples as forthe shorter boundary. FIG. 1 shows the location of the left and abovereconstructed samples and the sample of the current block involved inthe CCLM mode.

In some embodiments, this regression error minimization computation isperformed as part of the decoding process, not just as an encoder searchoperation, so no syntax is used to convey the α and β values.

In some embodiments, the CCLM prediction mode also includes predictionbetween the two chroma components, e.g., the Cr (red-difference)component is predicted from the Cb (blue-difference) component. Insteadof using the reconstructed sample signal, the CCLM Cb-to-Cr predictionis applied in residual domain. This is implemented by adding a weightedreconstructed Cb residual to the original Cr intra prediction to formthe final Cr prediction:

pred_(Cr)*(i,j)=pred_(Cr)(i,j)+α·resi_(Cb)′(i,j)  (4)

Here, resi_(Cb)′(i,j) presents the reconstructed Cb residue sample atposition (i,j).

In some embodiments, the scaling factor α may be derived in a similarway as in the CCLM luma-to-chroma prediction. The only difference is anaddition of a regression cost relative to a default α value in the errorfunction so that the derived scaling factor is biased towards a defaultvalue of −0.5 as follows:

$\begin{matrix}{\alpha = \frac{{N \cdot {\Sigma\left( {{{Cb}(n)} \cdot {{Cr}(n)}} \right)}} - {\Sigma\;{{{Cb}(n)} \cdot \Sigma}\;{{Cr}(n)}} + {\lambda \cdot \left( {- 0.5} \right)}}{{N \cdot {\Sigma\left( {{{Cb}(n)} \cdot {{Cb}(n)}} \right)}} - {\Sigma\;{{{Cb}(n)} \cdot \Sigma}\;{{Cb}(n)}} + \lambda}} & (5)\end{matrix}$

Here, Cb(n) represents the neighboring reconstructed Cb samples, Cr(n)represents the neighboring reconstructed Cr samples, and λ is equal toΣ(Cb(n)·Cb(n))>>9.

In some embodiments, the CCLM luma-to-chroma prediction mode is added asone additional chroma intra prediction mode. At the encoder side, onemore RD cost check for the chroma components is added for selecting thechroma intra prediction mode. When intra prediction modes other than theCCLM luma-to-chroma prediction mode is used for the chroma components ofa CU, CCLM Cb-to-Cr prediction is used for Cr component prediction.

In JEM and VTM-2.0, the total number of training sample in CCLM must bein a form of 2N. Suppose the current block size is W×H. If W is notequal to H, then the down-sampled luma sample set with more samples aredecimated to match the number of samples in the down-sampled luma sampleset with less samples.

1.2 Examples of Multiple Model CCLM

In the JEM, there are two CCLM modes: the single model CCLM mode and themultiple model CCLM mode (MMLM). As indicated by the name, the singlemodel CCLM mode employs one linear model for predicting the chromasamples from the luma samples for the whole CU, while in MMLM, there canbe two models.

In MMLM, neighboring luma samples and neighboring chroma samples of thecurrent block are classified into two groups, each group is used as atraining set to derive a linear model (i.e., a particular α and β arederived for a particular group). Furthermore, the samples of the currentluma block are also classified based on the same rule for theclassification of neighboring luma samples.

FIG. 2 shows an example of classifying the neighboring samples into twogroups. Threshold is calculated as the average value of the neighboringreconstructed luma samples. A neighboring sample withRec′_(L)[x,y]<=Threshold is classified into group 1; while a neighboringsample with Rec′_(L)[x,y]>Threshold is classified into group 2.

$\begin{matrix}{\quad\left\{ \begin{matrix}{{{Pred}_{c}\left\lbrack {x,y} \right\rbrack} = {{\alpha_{1} \times {{Rec}_{L}^{\prime}\left\lbrack {x,y} \right\rbrack}} + \beta_{1}}} & {{{if}\mspace{14mu}{{Rec}_{L}^{\prime}\left\lbrack {x,y} \right\rbrack}} \leq {Threshold}} \\{{{Pred}_{c}\left\lbrack {x,y} \right\rbrack} = {{\alpha_{2} \times {{Rec}_{L}^{\prime}\left\lbrack {x,y} \right\rbrack}} + B_{2}}} & {{{if}\mspace{14mu}{{Rec}_{L}^{\prime}\left\lbrack {x,y} \right\rbrack}} > {Threshold}}\end{matrix} \right.} & (6)\end{matrix}$

1.3 Examples of Downsampling Filters in CCLM

In some embodiments, and to perform cross-component prediction, for the4:2:0 chroma format, where 4 luma samples corresponds to 1 chromasamples, the reconstructed luma block needs to be downsampled to matchthe size of the chroma signal. The default downsampling filter used inCCLM mode is as follows:

Rec′ _(L)[x,y]={2×Rec _(L)[2x,2y]+2×Rec _(L)[2x,2y+1]+Rec_(L)[2x−1,2y]+Rec _(L)[2x+1,2y]+Rec _(L)[2x−1,2y+1]+Rec_(L)[2x+1,2y+1]+4}>>3  (7)

Here, the downsampling assumes the “type 0” phase relationship as shownin FIG. 3A for the positions of the chroma samples relative to thepositions of the luma samples, e.g., collocated sampling horizontallyand interstitial sampling vertically.

The exemplary 6-tap downsampling filter defined in (6) is used as thedefault filter for both the single model CCLM mode and the multiplemodel CCLM mode.

In some embodiments, and for the MMLM mode, the encoder canalternatively select one of four additional luma downsampling filters tobe applied for prediction in a CU, and send a filter index to indicatewhich of these is used. The four selectable luma downsampling filtersfor the MMLM mode, as shown in FIG. 3B, are as follows:

Rec′ _(L)[x,y]=(Rec _(L)[2x,2y]+Rec _(L)[2x+1,2y]+1)>>1  (8)

Rec′ _(L)[x,y]=(Rec _(L)[2x+1,2y]+Rec _(L)[2x+1,2y+1]+1)>>1  (9)

Rec′ _(L)[x,y]=(Rec _(L)[2x,2y+1]+Rec _(i),[2x+1,2y+1]+1)>>1  (10)

Rec′ _(L)[x,y]=(Rec _(L)[2x,2y]+Rec _(L)[2x,2y+1]+Rec _(L)[2x+1,2y]+Rec_(L)[2x+1,2y+1]+2)>>2  (11)

1.4 Exemplary Embodiments Related to Cross-Component Prediction

Previously proposed CCLM methods include, but are not limited to:

-   -   Only requiring neighboring luma samples used in normal        intra-prediction; and    -   Not needing to downsample the luma samples, or downsampling is        performed by a simple two-sample averaging.

The previously proposed examples described below assume that the colorformat is 4:2:0. As shown in FIG. 3A, one chroma (Cb or Cr) sample(represented by a triangle) corresponds to four luma (Y) samples(represented by circles): A, B, C and D as shown in FIG. 4. FIG. 5 showsan example of samples of a 4×4 chroma block with neighboring samples,and the corresponding luma samples.

Example 1. In one example, it is proposed that CCLM is done withoutdown-sampling filtering on luma samples.

(a) In one example, down-sampling process of neighboring luma samples isremoved in the CCLM parameter (e.g., α and β) derivation process.Instead, the down-sampling process is replaced by sub-sampling processwherein non-consecutive luma samples are utilized.

(b) In one example, down-sampling process of samples in the co-locatedluma block is removed in the CCLM chroma prediction process. Instead,only partial luma samples in the co-located luma block is used to derivethe prediction block of chroma samples.

(c) FIGS. 6A-6J show examples on an 8×8 luma block corresponding to a4×4 chroma block.

(d) In one example as shown in FIG. 6A, Luma sample at position “C” inFIG. 4 is used to correspond to the chroma sample. The above neighboringsamples are used in the training process to derive the linear model.

(e) In one example as shown in FIG. 6B, luma sample at position “C” inFIG. 4 is used to correspond to the chroma sample. The above neighboringsamples and above-right neighboring samples are used in the trainingprocess to derive the linear model.

(f) In one example as shown in FIG. 6C, luma sample at position “D” inFIG. 4 is used to correspond to the chroma sample. The above neighboringsamples are used in the training process to derive the linear model.

(g) In one example as shown in FIG. 6D, luma sample at position “D” inFIG. 4 is used to correspond to the chroma sample. The above neighboringsamples and above-right neighboring samples are used in the trainingprocess to derive the linear model.

(h) In one example as shown in FIG. 6E, luma sample at position “B” inFIG. 4 is used to correspond to the chroma sample. The left neighboringsamples are used in the training process to derive the linear model.

(i) In one example as shown in FIG. 6F, luma sample at position “B” inFIG. 4 is used to correspond to the chroma sample. The left neighboringsamples and left-bottom neighboring samples are used in the trainingprocess to derive the linear model.

(j) In one example as shown in FIG. 6G, luma sample at position “D” inFIG. 4 is used to correspond to the chroma sample. The left neighboringsamples are used in the training process to derive the linear model.

(k) In one example as shown in FIG. 6H, luma sample at position “D” inFIG. 4 is used to correspond to the chroma sample. The left neighboringsamples and left-bottom neighboring samples are used in the trainingprocess to derive the linear model.

(l) In one example as shown in FIG. 6I, luma sample at position “D” inFIG. 4 is used to correspond to the chroma sample. The above neighboringsamples and left neighboring samples are used in the training process toderive the linear model.

(m) In one example as shown in FIG. 6J, luma sample at position “D” inFIG. 4 is used to correspond to the chroma sample. The above neighboringsamples, left neighboring samples, above-right neighboring samples andleft-bottom neighboring samples are used in the training process toderive the linear model.

Example 2. In one example, it is proposed that CCLM only requiresneighboring luma samples which are used in normal intra-predictionprocess, that is, other neighboring luma samples are disallowed to beused in the CCLM process. In one example, CCLM is done with 2-tapfiltering on luma samples. FIGS. 7A-7D show examples on an 8×8 lumablock corresponding to a 4×4 chroma block.

(a) In one example as shown in FIG. 7A, luma sample at position “C” andposition “D” in FIG. 4 are filtered as F(C, D) to be used to correspondto the chroma sample. The above neighboring samples are used in thetraining process to derive the linear model.

(b) In one example as shown in FIG. 7B, luma sample at position “C” andposition “D” in FIG. 4 are filtered as F(C, D) to be used to correspondto the chroma sample. The above neighboring samples and above-rightneighboring samples are used in the training process to derive thelinear model.

(c) In one example as shown in FIG. 7C, luma sample at position “B” andposition “D” in FIG. 4 are filtered as F(B, D) to be used to correspondto the chroma sample. The left neighboring samples are used in thetraining process to derive the linear model.

(d) In one example as shown in FIG. 7D, luma sample at position “B” andposition “D” in FIG. 4 are filtered as F(B, D) to be used to correspondto the chroma sample. The left neighboring samples and left-bottomneighboring samples are used in the training process to derive thelinear model.

(e) In one example, F is defined as F(X,Y)=(X+Y)>>1. Alternatively,F(X,Y)=(X+Y+1)>>1.

Example 3. In one example, the previously proposed CCLM methods (e.g.,Examples 1 and 2 in this Section) can be applied in a selective way.That is, different block within a region, a slice, a picture or asequence may choose different kinds of previously proposed CCLM methods.

-   -   (a) In one embodiment, the encoder selects one kind of        previously proposed CCLM method from a predefined candidate set        and signals it to the decoder.        -   (i) For example, the encoder can select between Example 1(a)            and Example 1(e). Alternatively, it can select between            Example 1(b) and Example 1(f). Alternatively, it can select            between Example 1(c) and Example 1(g). Alternatively, it can            select between Example 1(d) and Example 1(h). Alternatively,            it can select between Example 2(a) and Example 2(c).            Alternatively, it can select between Example 2(b) and            Example 2(d).        -   (ii) The candidate set to be selected from and the signaling            may depend on the shape or size of the block. Suppose W and            H represent the width and height of the chroma block, T1,            and T2 are integers.            -   (1) In one example, if W<=T1 and H<=T2, there is no                candidate, e.g., CCLM is disabled. For example, T1=T2=2.            -   (2) In one example, if W<=T1 or H<=T2, there is no                candidate, e.g., CCLM is disabled. For example, T1=T2=2.            -   (3) In one example, if W×H<=T1, there is no candidate,                e.g., CCLM is disabled. For example, T1=4.            -   (4) In one example, if W<=T1 and H<=T2, there is only                one candidate such as Example 1(i). No CCLM method                selection information is signaled. For example, T1=T2=4.            -   (5) In one example, if W<=T1 or H<=T2, there is only one                candidate such as Example 1(i). No CCLM method selection                information is signaled. For example, T1=T2=4.            -   (6) In one example, if W×H<=T1, there is only one                candidate such as Example 1(i). No CCLM method selection                information is signaled. For example, T1=16.            -   (7) In one example, if W>H, there is only one candidate                such as Example 1(a). No CCLM method selection                information is signaled. Alternatively, if W>H (or W>N*H                wherein N is a positive integer), only candidates (or                some candidates) using above or/and above-right                neighboring reconstructed samples in deriving CCLM                parameters are included in the candidate set.            -   (8) In one example, if W<H, there is only one candidate                such as Example 1(e). No CCLM method selection                information is signaled. Alternatively, if W<H (or                N*W<H), only candidates (or some candidates) using left                or/and left-bottom neighboring reconstructed samples in                deriving CCLM parameters are included in the candidate                set.

(b) In one embodiment, both the encoder and decoder select a previouslyproposed CCLM method based the same rule. The encoder does not signal itto the decoder. For example, the selection may depend on the shape orsize of the block. In one example, if the width is larger than theheight, Example 1(a) is selected, otherwise, Example 1(e) is selected.

(c) One or multiple sets of previously proposed CCLM candidates may besignaled in sequence parameter set/picture parameter set/sliceheader/CTUs/CTBs/groups of CTUs.

Example 4. In one example, it is proposed that multiple CCLM methods(e.g., Examples 1 and 2) may be applied to the same chroma block. Thatis, one block within a region/slice/picture/sequence may choosedifferent kinds of previously proposed CCLM methods to derive multipleintermediate chroma prediction blocks and the final chroma predictionblock is derived from the multiple intermediate chroma predictionblocks.

-   -   (a) Alternatively, multiple sets of CCLM parameters (e.g., α and        β) may be firstly derived from multiple selected CCLM methods.        One final set of CCLM parameters may be derived from the        multiple sets and utilized for chroma prediction block        generation process.    -   (b) The selection of multiple CCLM methods may be signaled        (implicitly or explicitly) in a similar way as described in        Example 3.    -   (c) Indication of the usage of the proposed method may be        signaled in sequence parameter set/picture parameter set/slice        header/groups of CTUs/CTUs/coding blocks.

Example 5. In one example, whether and how to apply the previouslyproposed CCLM methods may depend on the position of the current block.

-   -   (a) In one example, one or more of the proposed methods is        applied on CUs that locate at the top boundary of the current        CTU as shown in FIG. 8A.    -   (b) In one example, one or more of the proposed methods is        applied on CUs that locate at the left boundary of the current        CTU as shown in FIG. 8B.    -   (c) In one example, one or more of the proposed methods is        applied in both the above cases.

1.5 Examples of CCLM in VVC

In some embodiments, CCLM as in JEM is adopted in VTM-2.0, but MM-CCLMin JEM is not adopted in VTM-2.0.

CCLM in VTM-5.0

In VTM-5.0, two additional CCLM modes (LM-A and LM-T) proposed inJVET-L0338 are adopted besides LM mode. LM-A only uses neighboringsamples above or right-above the current block and LM-T only usesneighboring samples left or left-below the current block, to derive theCCLM parameters.

Decoding Process of CCLM

In VTM-5.0, the LM derivation process is simplified to a 4-point max-minmethod proposed in WET-N0271. The corresponding working draft is shownin below.

Specification of INTRA_LT_CCLM, INTRA_L_CCLM and INTRA_T_CCLM IntraPrediction Mode

Inputs to this process are:

-   -   the intra prediction mode predModeIntra,    -   a sample location (xTbC, yTbC) of the top-left sample of the        current transform block relative to the top-left sample of the        current picture,    -   a variable nTbW specifying the transform block width,    -   a variable nTbH specifying the transform block height,    -   chroma neighboring samples p[x][y], with x=−1, y=0 . . .        2*nTbH−1 and x=0 . . . 2*nTbW−1, y=−1.        Output of this process are predicted samples predSamples[x][y],        with x=0 . . . nTbW−1, y=0 . . . nTbH−1.

The current luma location (xTbY, yTbY) is derived as follows:

(xTbY,yTbY)=(xTbC<<1,yTbC<<1)  (8-156)

The variables availL, availT and availTL are derived as follows:

-   -   The availability of left neighboring samples derivation process        for a block as specified in clause 6.4.X [Ed. (BB): Neighboring        blocks availability checking process tbd] is invoked with the        current chroma location (xCurr,yCurr) set equal to (xTbC,yTbC)        and the neighboring chroma location (xTbC−1,yTbC) as inputs, and        the output is assigned to availL.    -   The availability of top neighboring samples derivation process        for a block as specified in clause 6.4.X [Ed. (BB): Neighboring        blocks availability checking process tbd] is invoked with the        current chroma location (xCurr,yCurr) set equal to (xTbC,yTbC)        and the neighboring chroma location (xTbC,yTbC−1) as inputs, and        the output is assigned to availT.    -   The availability of top-left neighboring samples derivation        process for a block as specified in clause 6.4.X [Ed. (BB):        Neighboring blocks availability checking process tbd] is invoked        with the current chroma location (xCurr,yCurr) set equal to        (xTbC,yTbC) and the neighboring chroma location (xTbC−1,yTbC−1)        as inputs, and the output is assigned to availTL.    -   The number of available top-right neighboring chroma samples        numTopRight is derived as follows:        -   The variable numTopRight is set equal to 0 and availTR is            set equal to TRUE.        -   When predModeIntra is equal to INTRA_T_CCLM, the following            applies for x=nTbW . . . 2*nTbW−1 until availTR is equal to            FALSE or x is equal to 2*nTbW−1:            -   The availability derivation process for a block as                specified in clause 6.4.X [Ed. (BB): Neighboring blocks                availability checking process tbd] is invoked with the                current chroma location (xCurr,yCurr) set equal to                (xTbC,yTbC) and the neighboring chroma location                (xTbC+x,yTbC−1) as inputs, and the output is assigned to                availableTR            -   When availableTR is equal to TRUE, numTopRight is                incremented by one.    -   The number of available left-below neighboring chroma samples        numLeftBelow is derived as follows:        -   The variable numLeftBelow is set equal to 0 and availLB is            set equal to TRUE.        -   When predModeIntra is equal to INTRA_L_CCLM, the following            applies for y=nTbH . . . 2*nTbH−1 until availLB is equal to            FALSE or y is equal to 2*nTbH−1:            -   The availability derivation process for a block as                specified in clause 6.4.X [Ed. (BB): Neighboring blocks                availability checking process tbd] is invoked with the                current chroma location (xCurr,yCurr) set equal to                (xTbC,yTbC) and the neighboring chroma location                (xTbC−1,yTbC+y) as inputs, and the output is assigned to                availableLB            -   When availableLB is equal to TRUE, numLeftBelow is                incremented by one.                The number of available neighboring chroma samples on                the top and top-right numTopSamp and the number of                available neighboring chroma samples on the left and                left-below nLeftSamp are derived as follows:    -   If predModeIntra is equal to INTRA_LT_CCLM, the following        applies:

numSampT=availT?nTbW:0  (8-157)

numSampL=availL?nTbH:0  (8-158)

-   -   Otherwise, the following applies:

numSampT=(availT &&predModeIntra==INTRA_T_CCLM)?(nTbW+Min(numTopRight,nTbH)):0  (8-159)

numSampL=(availL &&predModeIntra==INTRA_L_CCLM)?(nTbH+Min(numLeftBelow,nTbW)):0  (8-160)

The variable bCTUboundary is derived as follows:

bCTUboundary=(yTbC &(1<<(Ctb Log 2SizeY−1)−1)==0)?TRUE:FALSE.   (8-161)

The variable cntN and array pickPosN with N being replaced by L and T,are derived as follows:

-   -   The variable numIs4N is set equal to ((availT && availL &&        predModeIntra==INTRA_LT_CCLM) ? 0:1).    -   The variable startPosN is set equal to numSampN>>(2+numIs4N).    -   The variable pickStepN is set equal to Max(1,        numSampN>>(1+numIs4N)).    -   If availN is equal to TRUE and predModeIntra is equal to        INTRA_LT_CCLM or INTRA_N_CCLM, the following assignments are        made:        -   cntN is set equal to Min(numSampN, (1+numIs4N)<<1)        -   pickPosN[pos] is set equal to (startPosN+pos*pickStepN),            with pos=0 . . . cntN−1.    -   Otherwise, cntN is set equal to 0.        The prediction samples predSamples[x][y] with x=0 . . . nTbW−1,        y=0 . . . nTbH−1 are derived as follows:    -   If both numSampL and numSampT are equal to 0, the following        applies:

predSamples[x][y]=1<<(BitDepth_(C)−1)  (8-162)

-   -   Otherwise, the following ordered steps apply:    -   1. The collocated luma samples pY[x][y] with x=0 . . . nTbW*2−1,        y=0 . . . nTbH*2−1 are set equal to the reconstructed luma        samples prior to the deblocking filter process at the locations        (xTbY+x, yTbY+y).    -   2. The neighboring luma samples samples pY[x][y] are derived as        follows:        -   When numSampL is greater than 0, the neighboring left luma            samples pY[x][y] with x=−1 . . . −3, y=0 . . . 2*numSampL−1,            are set equal to the reconstructed luma samples prior to the            deblocking filter process at the locations (xTbY+x, yTbY+y).        -   When numSampT is greater than 0, the neighboring top luma            samples pY[x][y] with x=0 . . . 2*numSampT−1, y=−1, −2, are            set equal to the reconstructed luma samples prior to the            deblocking filter process at the locations (xTbY+x, yTbY+y).        -   When availTL is equal to TRUE, the neighboring top-left luma            samples pY[x][y] with x=−1, y=−1, −2, are set equal to the            reconstructed luma samples prior to the deblocking filter            process at the locations (xTbY+x, yTbY+y).    -   3. The down-sampled collocated luma samples pDsY[x][y] with x=0        . . . nTbW−1, y=0 . . . nTbH−1 are derived as follows:        -   If sps_cclm_colocated_chroma_flag is equal to 1, the            following applies:            -   pDsY[x][y] with x=1 . . . nTbW−1, y=1 . . . nTbH−1 is                derived as follows:

pDsY[x][y]=(pY[2*x][2*y−1]+pY[2*x−1][2*y]+4*pY[2*x][2*y]+pY[2*x+1][2*y]+pY[2*x][2*y+1]+4)>>3  (8-163)

-   -   -   -   If availL is equal to TRUE, pDsY[0][y] with y=1 . . .                nTbH−1 is derived as follows:

pDsY[0][y]=(pY[0][2*y−1]+pY[−1][2*y]+4*pY[0][2*y]+pY[1][2*y]+pY[0][2*y+1]+4)>>3  (8-164)

-   -   -   -   Otherwise, pDsY[0][y] with y=1 . . . nTbH−1 is derived                as follows:

pDsY[0][y]=(pY[0][2*y−1]+2*pY[0][2*y]+pY[0][2*y+1]+2)>>2  (8-165)

-   -   -   -   If availT is equal to TRUE, pDsY[x][0] with x=1 . . .                nTbW−1 is derived as follows:

pDsY[x][0]=(pY[2*x][−1]+pY[2*x−1][0]+4*pY[2*x][0]+pY[2*x+1][0]+pY[2*x][1]+4)>>3  (8-166)

-   -   -   -   Otherwise, pDsY[x][0] with x=1 . . . nTbW 1 is derived                as follows:

pDsY[x][0]=(pY[2*x−1][0]+2*pY[2*x][0]+pY[2*x+1][0]+2)>>2  (8-167)

-   -   -   -   If availL is equal to TRUE and availT is equal to TRUE,                pDsY[0][0] is derived as follows:

pDsY[0][0]=(pY[0][−1]+pY[−1][0]+4*pY[0][0]+pY[1][0]+pY[0][1]+4)>>3  (8-168)

-   -   -   -   Otherwise if availL is equal to TRUE and availT is equal                to FALSE, pDsY[0][0] is derived as follows:

pDsY[0][0]=(pY[−1][0]+2*pY[0][0]+pY[1][0]+2)>>2  (8-169)

-   -   -   -   Otherwise if availL is equal to FALSE and availT is                equal to TRUE, pDsY[0][0] is derived as follows:

pDsY[0][0]=(pY[0][−1]+2*pY[0][0]+pY[0][1]+2)>>2  (8-170)

-   -   -   -   Otherwise (availL is equal to FALSE and availT is equal                to FALSE), pDsY[0][0] is derived as follows:

pDsY[0][0]=pY[0][0]  (8-171)

-   -   -   Otherwise, the following applies:            -   pDsY[x][y] with x=1 . . . nTbW−1, y=0 . . . nTbH−1 is                derived as follows:

pDsY[x][y]=(pY[2*x−1][2*y]+pY[2*x−1][2*y+1]+2*pY[2*x][2*y]+2*pY[2*x][2*y+1]+pY[2*x+1][2*y]+pY[2*x+1][2*y+1]+4)>>3  (8-172)

-   -   -   -   If availL is equal to TRUE, pDsY[0][y] with y=0 . . .                nTbH−1 is derived as follows:

pDsY[0][y]=(pY[−1][2*y]+pY[−1][2*y+1]+2*pY[0][2*y]+2*pY[0][2*y+1]+pY[1][2*y]+pY[1][2*y+1]+4)>>3  (8-173)

-   -   -   -   Otherwise, pDsY[0][y] with y=0 . . . nTbH−1 is derived                as follows:

pDsY[0][y]=(pY[0][2*y]+pY[0][2*y+1]+1)>>1  (8-174)

-   -   4. When numSampL is greater than 0, the selected neighboring        left chroma samples pSelC[idx] are set equal to        p[−1][pickPosL[idx]] with idx=0 . . . cntL−1, and the selected        down-sampled neighboring left luma samples pSelDsY[idx] with        idx=0 . . . cntL−1 are derived as follows:        -   The variable y is set equal to pickPosL[idx].        -   If sps_cclm_colocated_chroma_flag is equal to 1, the            following applies:            -   If y is greater than 0 or availTL is equal to TRUE,                pSelDsY[idx] is derived as follows:

pSelDsY[idx]=(pY[−2][2*y−1]+pY[−3][2*y]+4*pY[−2][2*y]+pY[−1][2*y]+pY[−2][2*y+1]+4)>>3  (8-175)

-   -   -   -   Otherwise:

pSelDsY[idx]=(pY[−3][0]+2*pY[−2][0]+pY[−1][0]+2)>>2  (8-177)

-   -   -   Otherwise, the following applies:

pSelDsY[idx]=(pY[−1][2*y]+pY[−1][2*y+1]+2*pY[−2][2*y]+2*pY[−2][2*y+1]+pY[−3][2*y]+pY[−3][2*y+1]+4)>>3  (8-178)

-   -   5. When numSampT is greater than 0, the selected neighboring top        chroma samples pSelC[idx] are set equal to        p[pickPosT[idx−cntL]][−1] with idx=cntL . . . cntL+cntT−1, and        the down-sampled neighboring top luma samples pSelDsY[idx] with        idx=0 . . . cntL+cntT−1 are specified as follows:        -   The variable x is set equal to pickPosT[idx−cntL].        -   If sps_cclm_colocated_chroma_flag is equal to 1, the            following applies:            -   If x is greater than 0, the following applies:                -   If bCTUboundary is equal to FALSE, the following                    applies:

pSelDsY[idx]=(pY[2*x][−3]+pY[2*x−1][−2]+4*pY[2*x][−2]+pY[2*x+1][−2]+pY[2*x][−1]+4)>>3  (8-179)

-   -   -   -   -   Otherwise (bCTUboundary is equal to TRUE), the                    following applies:

pSelDsY[idx]=(pY[2*x−1][−1]+2*pY[2*x][−1]+pY[2*x+1][−1]+2)>>2  (8-180)

-   -   -   -   Otherwise:                -   If availTL is equal to TRUE and bCTUboundary is                    equal to FALSE, the following applies:

pSelDsY[idx]=(pY[0][−3]+pY[−1][−2]+4*pY[0][−2]+pY[1][−2]+pY[0][−1]+4)>>3  (8-181)

-   -   -   -   -   Otherwise if availTL is equal to TRUE and                    bCTUboundary is equal to TRUE, the following                    applies:

pSelDsY[idx]=(pY[−1][−1]+2*pY[0][−1]+pY[1][−1]+2)>>2  (8-182)

-   -   -   -   -   Otherwise if availTL is equal to FALSE and                    bCTUboundary is equal to FALSE, the following                    applies:

pSelDsY[idx]=(pY[0][−3]+2*pY[0][−2]+pY[0][−1]+2)>>2  (8-183)

-   -   -   -   -   Otherwise (availTL is equal to FALSE and                    bCTUboundary is equal to TRUE), the following                    applies:

pSelDsY[idx]=pY[0][−1]  (8-184)

-   -   -   Otherwise, the following applies:            -   If x is greater than 0, the following applies:                -   If bCTUboundary is equal to FALSE, the following                    applies:

pSelDsY[idx]=(pY[2*x−1][−2]+pY[2*x−1][−1]+2*pY[2*x][−2]2*pY[2*x][−1]+pY[2*x+1][−2]+pY[2*x+1][−1][−1]+4)>>3  (8-185)

-   -   -   -   -   Otherwise (bCTUboundary is equal to TRUE), the                    following applies:

pSelDsY[idx]=(pY[2*x−1][−1]+2*pY[2*x][−1]+pY[2*x+1][−1]+2)>>2  (8-186)

-   -   -   -   Otherwise:                -   If availTL is equal to TRUE and bCTUboundary is                    equal to FALSE, the following applies:

pSelDsY[idx]=(pY[−1][−2]+pY[−1][−1]+2*pY[0][−2]+2*pY[0][−1]+pY[1][−2]+pY[1][−1]+4)>>3  (8-187)

-   -   -   -   -   Otherwise if availTL is equal to TRUE and                    bCTUboundary is equal to TRUE, the following                    applies:

pSelDsY[idx]=(pY[−1][−1]+2*pY[0][−1]+pY[1][−1]+2)>>2  (8-188)

-   -   -   -   -   Otherwise if availTL is equal to FALSE and                    bCTUboundary is equal to FALSE, the following                    applies:

pSelDsY[idx]=(pY[0][−2]+pY[0][−1]+1)>>1  (8-189)

-   -   -   -   -   Otherwise (availTL is equal to FALSE and                    bCTUboundary is equal to TRUE), the following                    applies:

pSelDsY[idx]=pY[0][−1]  (8-190)

-   -   6. When cntT+cntL is not equal to 0, the variables minY, maxY,        minC and maxC are derived as follows:        -   When cntT+cntL is equal to 2, set pSelComp[3] equal to            pSelComp[0], pSelComp[2] equal to pSelComp[1], pSelComp[0]            equal to pSelComp[1], and pSelComp[1] equal to pSelComp[3],            with Comp being replaced by DsY and C.        -   The arrays minGrpIdx and maxGrpIdx are set as follows:            -   minGrpIdx[0]=0.            -   minGrpIdx[1]=2.            -   maxGrpIdx[0]=1.            -   maxGrpIdx[1]=3.        -   When pSelDsY[minGrpIdx[0]] is greater than            pSelDsY[minGrpIdx[1]], minGrpIdx[0] and minGrpIdx[1] are            swapped as (minGrpIdx[0], minGrpIdx[1])=Swap(minGrpIdx[0],            minGrpIdx[1]).        -   When pSelDsY[maxGrpIdx[0]] is greater than            pSelDsY[maxGrpIdx[1]], maxGrpIdx[0] and maxGrpIdx[1] are            swapped as (maxGrpIdx[0], maxGrpIdx[1])=Swap(maxGrpIdx[0],            maxGrpIdx[1]).        -   When pSelDsY[minGrpIdx[0]] is greater than            pSelDsY[maxGrpIdx[1]], arrays minGrpIdx and maxGrpIdx are            swapped as (minGrpIdx, maxGrpIdx)=Swap(minGrpIdx,            maxGrpIdx).        -   When pSelDsY[minGrpIdx[1]] is greater than            pSelDsY[maxGrpIdx[0]], minGrpIdx[1] and maxGrpIdx[0] are            swapped as (minGrpIdx[1], maxGrpIdx[0])=Swap(minGrpIdx[1],            maxGrpIdx[0]).

maxY=(pSelDsY[maxGrpIdx[0]]+pSelDsY[maxGrpIdx[1]]+1)>>1.

maxC=(pSelC[maxGrpIdx[0]]+pSelC[maxGrpIdx[1]]+1)>>1.

minY=(pSelDsY[minGrpIdx[0]]+pSelDsY[minGrpIdx[1]]+1)>>1.

minC=(pSelC[minGrpIdx[0]]+pSelC[minGrpIdx[1]]+1)>>1.

-   -   7. The variables a, b, and k are derived as follows:        -   If numSampL is equal to 0, and numSampT is equal to 0, the            following applies:

k=0  (8-208)

a=0  (8-209)

b=1<<(BitDepth_(C)−1)  (8-210)

-   -   -   Otherwise, the following applies:

diff=maxY−minY  (8-211)

-   -   -   -   If diff is not equal to 0, the following applies:

diffC=maxC−minC  (8-212)

x=Floor(Log 2(diff))  (8-213)

normDiff=((diff<<4)>>x)&15  (8-214)

x+=(normDiff!=0)?1:0  (8-215)

y=Floor(Log 2(Abs(diffC)))+1  (8-216)

a=(diffC*(divSigTable[normDiff]8)+2Y ⁻¹)>>y  (8-217)

k=((3+x−y)<1)?1:3+x−y  (8-218)

a=((3+x−y)<1)?Sign(a)*15:a  (8-219)

b=minC−((a*minY)>>k)  (8-220)

-   -   -   -   -   where divSigTable[ ] is specified as follows:

divSigTable[ ]={0,7,6,5,5,4,4,3,3,2,2,1,1,1,1,0}  (8-221)

-   -   -   -   -   Otherwise (diff is equal to 0), the following                    applies:

k=0  (8-222)

a=0  (8-223)

b=minC  (8-224)

-   -   8. The prediction samples predSamples[x][y] with x=0 . . .        nTbW−1, y=0 . . . nTbH−1 are derived as follows:

predSamples[x][y]=Clip1C(((pDsY[x][y]*a)>>k)+b)  (8-225)

JVET-L0283

This contribution proposes Multiple Reference Line Intra Prediction(MRLIP) in which the directional intra-prediction can be generated byusing more than one reference line of neighboring samples adjacent ornon-adjacent to the current block.

1.6 Examples of Intra Prediction in VVC 1.6.1 Intra Mode Coding with 67Intra Prediction Modes

To capture the arbitrary edge directions presented in natural video, thenumber of directional intra modes is extended from 33, as used in HEVC,to 65. The additional directional modes are depicted as dotted arrows inFIG. 9, and the planar and DC modes remain the same. These denserdirectional intra prediction modes apply for all block sizes and forboth luma and chroma intra predictions.

Conventional angular intra prediction directions are defined from 45degrees to −135 degrees in clockwise direction as shown in FIG. 9. InVTM2, several conventional angular intra prediction modes are adaptivelyreplaced with wide-angle intra prediction modes for the non-squareblocks. The replaced modes are signaled using the original method andremapped to the indexes of wide angular modes after parsing. The totalnumber of intra prediction modes is unchanged (e.g., 67), and the intramode coding is unchanged.

In HEVC, every intra-coded block has a square shape and the length ofeach of its side is a power of 2. Thus, no division operations arerequired to generate an intra-predictor using DC mode. In VTV2, blockscan have a rectangular shape that necessitates the use of a divisionoperation per block in the general case. To avoid division operationsfor DC prediction, only the longer side is used to compute the averagefor non-square blocks.

1.6.2 Examples of Intra Mode Coding

In some embodiments, and to keep the complexity of the MPM listgeneration low, an intra mode coding method with 3 Most Probable Modes(MPMs) is used. The following three aspects are considered to the MPMlists:

-   -   Neighbor intra modes;    -   Derived intra modes; and    -   Default intra modes.

For neighbor intra modes (A and B), two neighboring blocks, located inleft and above are considered. An initial MPM list is formed byperforming pruning process for two neighboring intra modes. If twoneighboring modes are different each other, one of the default modes(e.g., PLANA (0), DC (1), ANGULAR50 (e.g., 50)) is added to the MPM listafter the pruning check with the existing two MPMs. When the twoneighboring modes are the same, either the default modes or the derivedmodes are added to the MPM list after the pruning check. The detailedgeneration process of three MPM list is derived as follows:

If two neighboring candidate modes (i.e., A==B) are same,

-   -   If A is less than 2, candModeList [3]={0, 1, 50}.    -   Otherwise, candModeList[0]={A, 2+((A+61) % 64), 2+((A−1)%64)}

Otherwise,

-   -   If neither of A and B is equal to 0, candModeList [3]={A, B, 0}.    -   Otherwise, if neither of A and B is equal to 1, candModeList        [3]={A, B, 1}.    -   Otherwise, candModeList [3]={A, B, 50}.

An additional pruning process is used to remove duplicated modes so thatonly unique modes can be included into the MPM list. For entropy codingof the 64 non-MPM modes, a 6-bit Fixed Length Code (FLC) is used.

1.6.3 Wide-Angle Intra Prediction for Non-Square Blocks

In some embodiments, conventional angular intra prediction directionsare defined from 45 degrees to −135 degrees in clockwise direction. InVTM2, several conventional angular intra prediction modes are adaptivelyreplaced with wide-angle intra prediction modes for non-square blocks.The replaced modes are signaled using the original method and remappedto the indexes of wide angular modes after parsing. The total number ofintra prediction modes for a certain block is unchanged, e.g., 67, andthe intra mode coding is unchanged.

To support these prediction directions, the top reference with length2W+1, and the left reference with length 2H+1, are defined as shown inthe examples in FIGS. 10A and 10B.

In some embodiments, the mode number of replaced mode in wide-angulardirection mode is dependent on the aspect ratio of a block. The replacedintra prediction modes are illustrated in Table 1.

TABLE 1 Intra prediction modes replaced by wide-angle modes ConditionReplaced intra prediction modes W/H == 2 Modes 2, 3, 4, 5, 6, 7 W/H > 2Modes 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 W/H == 1 None H/W == ½ Modes 61,62, 63, 64, 65, 66 H/W < ½ Mode 57, 58, 59, 60, 61, 62, 63, 64, 65, 66

As shown in FIG. 11, two vertically-adjacent predicted samples may usetwo non-adjacent reference samples in the case of wide-angle intraprediction. Hence, low-pass reference samples filter and side smoothingare applied to the wide-angle prediction to reduce the negative effectof the increased gap Δp_(α).

1.6.4 Examples of Position Dependent Intra Prediction Combination (PDPC)

In the VTM2, the results of intra prediction of planar mode are furthermodified by a position dependent intra prediction combination (PDPC)method. PDPC is an intra prediction method which invokes a combinationof the un-filtered boundary reference samples and HEVC style intraprediction with filtered boundary reference samples. PDPC is applied tothe following intra modes without signaling: planar, DC, horizontal,vertical, bottom-left angular mode and its eight adjacent angular modes,and top-right angular mode and its eight adjacent angular modes.

The prediction sample pred(x,y) is predicted using an intra predictionmode (DC, planar, angular) and a linear combination of reference samplesaccording to the Equation as follows:

pred(x,y)=(wL×R _(−1,y) +wT×R _(x,−1) −wTL×R_(−1,−1)+(64−wL−wT+wTL)×pred(x,y)+32)>>shift

Herein, R_(x,−1), R_(−1,y) represent the reference samples located atthe top and left of current sample (x, y), respectively, and R_(−1,−1)represents the reference sample located at the top-left corner of thecurrent block.

In some embodiments, and if PDPC is applied to DC, planar, horizontal,and vertical intra modes, additional boundary filters are not needed, asrequired in the case of HEVC DC mode boundary filter orhorizontal/vertical mode edge filters.

FIGS. 12A-12D illustrate the definition of reference samples (R_(x,−1),R_(−1,y) and R_(−1,−1)) for PDPC applied over various prediction modes.The prediction sample pred (x′, y′) is located at (x′, y′) within theprediction block. The coordinate x of the reference sample R_(x,−1) isgiven by: x=x′+y′+1, and the coordinate y of the reference sampleR_(−1, y) is similarly given by: y=x′+y′+1.

In some embodiments, the PDPC weights are dependent on prediction modesand are shown in Table 2, where S=shift.

TABLE 2 Examples of PDPC weights according to prediction modesPrediction modes wT wL wTL Diagonal 16 >> ((y′ << 1) >> S) 16 >> ((x′<< 1) >> S) 0 top-right Diagonal 16 >> ((y′ << 1) >> S) 16 >> ((x′<< 1) >> S) 0 bottom-left Adjacent diag. 32 >> ((y′ << 1) >> S) 0 0top-right Adjacent diag. 0 32 >> ((x′ << 1) >> S) 0 bottom-left

2 EXAMPLES OF DRAWBACKS IN EXISTING IMPLEMENTATIONS SOLVED BY DESCRIBEDTECHNIQUES

The current CCLM implementations in either JEM or VTM exhibit at leastthe following issues:

-   -   In the current CCLM design of JEM, it requires more neighboring        luma samples than what are used in normal intra-prediction. CCLM        requires two above neighboring rows of luma samples and three        left neighboring columns of luma samples. MM-CCLM requires four        above neighboring rows of luma samples and four left neighboring        columns of luma samples. This is undesirable in hardware design.    -   Other related methods use only one line of neighboring luma        samples, but they bring some coding performance loss.    -   The neighboring chroma samples are only used to derive LM        parameters. When generating the prediction block of a chroma        block, only luma samples and derived LM parameters are utilized.        Therefore, the spatial correlation between current chroma block        and its neighboring chroma blocks are not utilized.

In VTM-5.0, CCLM (including LM, LM-L, LM-T modes) may find only twoavailable neighboring chroma samples (and their corresponding lumasamples which may be down-sampled). That is a special case in the4-point max-min CCLM parameter derivation process, which is notdesirable.

3 EXEMPLARY METHODS FOR CROSS-COMPONENT PREDICTION IN VIDEO CODING

Embodiments of the presently disclosed technology overcome drawbacks ofexisting implementations, thereby providing video coding with highercoding efficiencies but lower computational complexity. Cross-componentprediction, based on the disclosed technology, may enhance both existingand future video coding standards, is elucidated in the followingexamples described for various implementations. The examples of thedisclosed technology provided below explain general concepts, and arenot meant to be interpreted as limiting. In an example, unlessexplicitly indicated to the contrary, the various features described inthese examples may be combined.

In the following examples and methods, the term “LM method” includes,but is not limited to, the LM mode in JEM or VTM, and MMLM mode in JEM,left-LM mode which only uses left neighboring samples to derive thelinear model, the above-LM mode which only uses above neighboringsamples to derive the linear model or other kinds of methods whichutilize luma reconstruction samples to derive chroma prediction blocks.

Example 1. In one example, consider methods for how to down-sample lumasamples depends on whether the luma samples are inside the current blockor outside the current block.

(a) The down-sampled luma samples may be used to derive LM parameters.Here, the luma block is the corresponding luma block of one chromablock.

(b) The down-sampled luma samples may be used to derive other kinds ofchroma prediction blocks. Here, the luma block is the corresponding lumablock of one chroma block.

(c) For example, luma samples inside the current block are down-sampledwith the same method as that in JEM, but luma samples outside thecurrent block are down-sampled with a different method.

Example 2. In one example, consider methods for how to down-sampleoutside luma samples that depend on their positions.

(a) In one example, the down-sampled luma samples may be used to deriveprediction blocks. Here, the outside luma samples could be neighboringluma samples or non-adjacent luma samples relative to the current lumablock to be coded.

(b) The down-sampled luma samples may be used to derive LM parameters.Here, outside luma samples are those which not located in thecorresponding luma block of the current chroma block.

(c) In one example, luma samples left to the current block or above tothe current block are down-sampled in different ways.

(d) In one example, luma samples are down-sampled as specified below asshown in FIGS. 13A and 13B.

-   -   (i) Luma samples inside the current block are down-sampled in        the same way as in JEM.    -   (ii) Luma samples outside the current block and above to the        current block are down-sampled to position C or D in FIG. 4.        Alternatively, Luma samples are down-sampled to position C in        FIG. 4 with a filter. Suppose the above luma samples adjacent to        the current block are denoted as a[i], then        d[i]=(a[2i−1]+2*a[2i]+a[2i+1]+2)>>2, where d[i] represents the        down-sampled luma samples.        -   (1) If the sample a[2i−1] is unavailable,            d[i]=(3*a[2i]+a[2i+1]+2)>>2        -   (2) If the sample a[2i+1] is unavailable,            d[i]=(a[2i−1]+3*a[2i]+2)>>2    -   (iii) Luma samples outside the current block and left to the        current block are down-sampled to position B or D in FIG. 4.        Alternatively, Luma samples are down-sampled to the half        position between B and D. Suppose the luma samples left adjacent        to the current block are denoted as a[j], then        d[j]=(a[2j]+a[2j+1]+1)>>1, where d[j] represents the        down-sampled luma samples.    -   (iv) In one example, W luma down-sampled samples are generated,        where W is the width of the current chroma block as shown in        FIG. 13A.        -   (1) Alternatively, N×W luma down-sampled samples are            generated from the above adjacent luma samples, where Nis an            integer such as 2, as shown in FIG. 13B.        -   (2) Alternatively, W+K luma down-sampled samples are            generated from the above adjacent luma samples, wherein K is            a positive integer.        -   (3) Alternatively, W/N luma down-sampled samples are            generated from the above adjacent luma samples, where Nis an            integer such as 2.    -   (v) In one example, H luma down-sampled samples are generated        from the left adjacent luma samples, where H is the height of        the current chroma block as shown in FIG. 13A.        -   (1) Alternatively, N×H luma down-sampled samples are            generated, where Nis an integer such as 2, as shown in FIG.            13B.        -   (2) Alternatively, H+K luma down-sampled samples are            generated, wherein K is a positive integer.        -   (3) Alternatively, H/N luma down-sampled samples are            generated, where N is an integer such as 2.

Example 3. In one example, consider methods for how to select and howmany samples to be down-sampled for outside luma/inside luma samples maydepend on the block size/block shape.

Example 4. In one example, the prediction block generated from LMmethods may be further refined before being used as the predictor ofchroma block.

-   -   (a) In one example, the reconstructed chroma samples        (neighboring or non-adjacent chroma samples) may be further        utilized together with the prediction block from LM methods.        -   (i) In one example, a linear function may be applied with            the neighboring reconstructed chroma samples and the            prediction block from LM methods as input and refined            prediction samples as output.        -   (ii) In one example, for certain positions, the prediction            block from LM methods may be refined and for the remaining            positions, the prediction block from LM methods may be            directly inherited without being refined.    -   (b) In one example, two prediction blocks may be generated        (e.g., one from LM methods and the other one from chroma intra        prediction block). However, the final prediction block may be        generated from the two prediction blocks for certain positions        and directly copied from the prediction block from LM for the        remaining positions.        -   (i) In one example, the chroma intra prediction mode may be            signaled or derived from luma intra prediction modes.        -   (ii) In one example, ‘the certain positions’ where two            prediction blocks may be jointly used includes the top            several rows and/or left several columns.    -   (c) For example, boundary filtering can be applied to LM mode,        MMLM mode, left-LM mode or above-LM mode, no matter what kind of        down-sampling filter is applied.    -   (d) In one example, a function of prediction block from LM        methods and reconstructed chroma samples above current block may        be utilized together to refine the prediction block from LM        methods.        -   (i) Suppose the reconstructed chroma samples above adjacent            to the current block are denoted as a[−1][j], the LM            predicted sample at the ith row and jth column is a[i][j],            then the prediction sample after boundary filtering is            calculated as a function of a[−1][j] and a[i][j]. In one            example, the final prediction for the (i,j) position is            defined as a′[i][j]=(w1*a[i][j]+w2*a[−1][i]+2^(N-1))>>N,            where w1+w2=2^(N).        -   (ii) The boundary filtering can only be applied when above            neighboring samples are available.        -   (iii) In one example, the boundary filtering is only applied            if i<=K. K is an integer such as 0 or 1. For example, K=0,            w1=w2=1. In another example, K=0, w1=3, w2=1.        -   (iv) In one example, w1 and w2 depend on the row index (i).            For example, K=1, w1=w2=1 for samples a[0][j], but w1=3 and            w2=1 for samples a[1][j].    -   (e) In one example, a function of prediction block from LM        methods and reconstructed chroma samples left to the current        block may be utilized together to refine the prediction block        from LM methods.        -   (i) Suppose the reconstructed chroma samples left adjacent            to the current block are denoted as a[i][−1], the LM            predicted sample at the ith row and jth column is a[i][j],            then the prediction sample after boundary filtering is            calculated as a function of a[i][−1] and a[i][j]. In one            example, the final prediction for the (i,j) position is            defined as a′[i][j]=(w1*a[i][j]+w2*a[i][−1]+2^(N-1))>>N,            where w1+w2=2^(N).        -   (ii) In one example, the boundary filtering can only be            applied when left neighboring samples are available.        -   (iii) In one example, the boundary filtering is only applied            if j<=K. K is an integer such as 0 or 1. For example, K=0,            w1=w2=1. In another example, K=0, w1=3, w2=1.        -   (iv) In one example, w1 and w2 depend on the column index            (i). For example, K=1, w1=w2=1 for samples a[0][j], but w1=3            and w2=1 for samples a[1][j].    -   (f) In one example, a function of prediction block from LM        methods and reconstructed chroma samples left to and above the        current block may be utilized together to refine the prediction        block from LM methods.        -   (i) Suppose the reconstructed chroma samples above adjacent            to the current block are denoted as a[−1][j], the            reconstructed chroma samples left adjacent to the current            block are denoted as a[i][−1], and the LM predicted sample            at the ith row and jth column is a[i][j], then the            prediction sample after boundary filtering is calculated as            a′[i][j]=(w1*a[i][j]+w2*a[i][−1]+w3*a[−1][j]+2^(N-1))>>N,            where w1+w2+w3=2^(N).        -   (ii) In one example, the boundary filtering can only be            applied when both left and above neighboring samples are            available.        -   (iii) In one example, this boundary filtering is only            applied when i<=K and j<=P. In another example, it is only            applied when i<=K or J<=P.        -   (iv) In one example, this boundary filtering is only applied            to a[0][0] with w1=2, w2=w3=1.

Example 5. In one example, consider whether to apply and how to apply LMmethods may depend on the size or shape of the current block. Assume Wand H represent the width and height of the current chroma block,respectively, and T1 and T2 are thresholds.

-   -   (a) In one example, LM mode (or MMLM mode, or Left-LM mode, or        Above-LM mode) is not applicable when W<=T1 and H<=T2. For        example, T1=T2=4.    -   (b) In one example, LM mode (or MMLM mode, or Left-LM mode, or        Above-LM mode) is not applicable when W<=T1 or H<=T2. For        example, T1=T2=2.    -   (c) In one example, LM mode (or MMLM mode, or Left-LM mode, or        Above-LM mode) is not applicable when W<=T1 or H<=T2. For        example, T1=T2=4.    -   (d) In one example, LM (or MMLM mode, or Left-LM mode, or        Above-LM mode) mode is not applicable when W+H<=T1. For example,        T1=6.    -   (e) In one example, LM mode (or MMLM mode, or Left-LM mode, or        Above-LM mode) is not applicable when W×H<=T1. For example,        T1=16.    -   (f) In one example, Left-LM mode is not applicable when H<=T1.        For example, T1=4.    -   (g) In one example, Above-LM mode is not applicable when W<=T1.        For example, T1=4.    -   (h) T1 and/or T2 may be pre-defined or signaled in an SPS, a        sequence header, a PPS, a picture header, a VPS, a slice header,        a CTU, a CU or a group of CTUs.

Example 6. In one example, consider whether and how to apply theproposed single-line LM methods may depend on the position of thecurrent block.

-   -   (a) In one example, one or more of the proposed methods is        applied on CUs that locate at the top boundary of the current        CTU as shown in FIG. 8A.    -   (b) In one example, one or more of the proposed methods is        applied on CUs that locate at the left boundary of the current        CTU as shown in FIG. 8B.    -   (c) In one example, one or more of the proposed methods is        applied in both the above cases.    -   (d) In one example, one or more of the proposed methods is        applied on CUs that locate at the top boundary of a region such        as a 64×64 block.    -   (e) In one example, one or more of the proposed methods is        applied on CUs that locate at the left boundary of a region such        as a 64×64 block.

The examples described above may be incorporated in the context of themethods described below, e.g., method 1400, which may be implemented ata video encoder and/or decoder.

Example 7: The above LM neighboring samples are down-sampled by onefiltering method (e.g., the one defined in bullet 2.d.ii), and thoseluma samples which may be used for normal intra prediction process(e.g., 2 W samples (above and above right of current block) in VVC) areutilized. While the left LM neighboring samples are down-sampled by adifferent filtering method (e.g., the one defined in JEM or VTM-2.0).

-   -   (a) FIG. 16 shows an example how to combine different        down-sampling methods together.

Example 8: In an alternative example to bullet 2.d.ii.1, Luma samplesare down-sampled to position C in FIG. 4 with a filter. Suppose theabove luma samples adjacent to the current block are denoted as a[i],then d[i]=(a[2i−1]+2*a[2i]+a[2i+1]+offset0)>>2, if i>0; Otherwise (ifi==0), d[i]=(3*a[2i]+a[2i+1]+offset1)>>2.

-   -   (a) Alternatively, if i==0, d[i]=a[2i].    -   (b) Alternatively, if i==0, d[i]=(a[2i]+a[2i+1]+offset2)>>1.    -   (c) In one example, offset0=offset1=2; offset2=1.

Example 9: In some embodiments, the number of down-sampling luma samplesabove or left to the current block may depend on the dimension of thecurrent block. Suppose the width and height of the current chroma blockare denoted as W and H:

-   -   (a) For example, W luma samples above the current block are        down-sampled and H luma samples left to the current block are        down-sampled if W==H;    -   (b) For example, 2*W luma samples above the current block are        down-sampled and 2*H luma samples left to the current block are        down-sampled if W==H;    -   (c) For example, 2*W luma samples above the current block are        down-sampled and H luma samples left to the current block are        down-sampled if W<H;    -   (d) For example, 2*W luma samples above the current block are        down-sampled and H luma samples left to the current block are        down-sampled if W<=H;    -   (e) For example, 2*W luma samples above the current block are        down-sampled and H luma samples left to the current block are        down-sampled if W>H;    -   (f) For example, 2*W luma samples above the current block are        down-sampled and H luma samples left to the current block are        down-sampled if W>=H;    -   (g) For example, W luma samples above the current block are        down-sampled and 2*H luma samples left to the current block are        down-sampled if W<H;    -   (h) For example, W luma samples above the current block are        down-sampled and 2*H luma samples left to the current block are        down-sampled if W<=H;    -   (i) For example, W luma samples above the current block are        down-sampled and 2*H luma samples left to the current block are        down-sampled if W>H;    -   (j) For example, W luma samples above the current block are        down-sampled and 2*H luma samples left to the current block are        down-sampled if W>=H;    -   (k) For example, 2*W luma samples above the current block are        down-sampled only if both the above neighboring blocks and        right-above neighboring blocks are all available. Otherwise, W        luma samples above the current block are down-sampled.    -   (l) For example, 2*H luma samples left to the current block are        down-sampled only if both the left neighboring blocks and        left-bottom neighboring blocks are all available. Otherwise, H        luma samples above the current block are down-sampled.    -   (m) Suppose there are W′ (W′ is equal to W or 2*W) down-sampled        luma sample above to the current block, H′ (H′ is equal to H or        2*H) down-sampled luma sample above to the current block. If W′        is not equal to H′, then the down-sampled luma sample set with        more samples are decimated to match the number of samples in the        down-sampled luma sample set with less samples, as defined in        JEM or VTM-2.0.    -   (n) In one example, the number of down-sampled luma samples        above and left to the current block is set to max (W, H).        -   (i) Alternatively, the number of down-sampled luma samples            above and left to the current block is set to a value            between min(W,H) and max (W, H).        -   (ii) Alternatively, the number of down-sampled luma samples            above and left to the current block is set to a value            between min(W,H), max (W, H))*SCALEDVALUE. For example,            SCALEDVALUE is set to 2.

Example 10: In some embodiments, the decimation process when W is notequal to H is different from that in JEM or VTM-2.0.

-   -   (a) For example, if W>H, the H leftmost above neighboring        samples and H left neighboring samples are involved in the        training process.    -   (b) For example, if W>H, the H rightmost above neighboring        samples and H left neighboring samples are involved in the        training process.    -   (c) For example, if W>H, and W=H*n, then W above neighboring        samples and H left neighboring samples are involved in the        training process, and each left neighboring sample appears n        times in the training process.    -   (d) For example, if W<H, the W topmost left neighboring samples        and W above neighboring samples are involved in the training        process.    -   (e) For example, if W<H, the W bottommost left neighboring        samples and W above neighboring samples are involved in the        training process.    -   (f) For example, if W<H, and H=W*n, then W above neighboring        samples and H left neighboring samples are involved in the        training process, and each above neighboring sample appears n        times in the training process.

Example 11

Whether and/or how a CCLM mode (such as LM, LM-A, LM-T) is applied for ablock may depend on the number of available neighboring samples and/ordimensions of the block.

-   -   a. In one example, the block may refer to a chroma coding block.    -   b. In one example, if one or multiple specific CCLM modes (such        as LM, LM-A, LM-T) is not applicable for a block (e.g.,        according to the number of available neighboring samples and/or        dimensions), the syntax element(s) (such as a flag or a mode        representation) to indicate the specific CCLM mode(s) may not be        signaled, and the specific CCLM mode(s) are inferred to be not        applied.    -   c. In one example, if one or multiple specific CCLM modes (such        as LM, LM-A, LM-T) is not applicable for a block (e.g.,        according to the number of available neighboring samples and/or        dimensions), the syntax element(s) (such as a flag or a mode        representation) to indicate the CCLM mode(s) may be signaled and        should indicate that the CCLM mode(s) are not applied in a        conformance bitstream.    -   d. In one example, if one or multiple specific CCLM modes (such        as LM, LM-A, LM-T) is not applicable for a block (e.g.,        according to the number of available neighboring samples and/or        dimensions), the syntax element(s) (such as a flag or a mode        representation) to indicate the CCLM mode(s) may be signaled but        the signaling may be ignored by the decoder and the specific        CCLM mode(s) are inferred to be not applied.    -   e. In one example, the neighboring samples may refer to chroma        neighboring samples.        -   i. Alternatively, the neighboring samples may refer to            corresponding luma neighboring samples, which may be            down-sampled (e.g., according to the color format).    -   f. In one example, if one or multiple specific CCLM modes (such        as LM, LM-A, LM-T) is not applicable for a block (e.g.,        according to the number of available neighboring samples and/or        dimensions) but a specific CCLM mode is signaled, the parameter        derivation process for CCLM is done in a specific way.        -   i. In one example, the parameter a is set equal to 0 and the            parameter b is set equal to a fixed number, such as            1<<(BitDepth−1).    -   g. In one example, LM mode and/or LM-A mode and/or LM-T mode are        not applicable when the number of available neighboring samples        is less than T, where T is an integer such as 4.    -   h. In one example, LM mode is not applicable if the width of the        block is equal to 2 and the left neighboring block is        unavailable.    -   i. In one example, LM mode is not applicable if the height of        the block is equal to 2 and the above neighboring block is        unavailable.    -   j. In one example, LM-T mode is not applicable if the width of        the block is equal to 2.        -   i. Alternatively, LM-T mode is not applicable if the width            of the block is equal to 2 and the right-above neighboring            block is unavailable.    -   k. In one example, LM-L mode is not applicable if the height of        the block is equal to 2.        -   i. Alternatively, LM-L mode is not applicable if the height            of the block is equal to 2 and the left-below neighboring            block is unavailable.    -   l. In one example, LM-L mode is not applicable if LM mode is not        applicable.    -   m. In one example, LM-T mode is not applicable if LM mode is not        applicable.    -   n. In one example, the ‘available neighboring samples’ may be        those from existing above and/or left samples according to the        selected reference lines.    -   o. In one example, the ‘available neighboring samples’ may be        those from selected positions according to the selected        reference lines and the rule of CCLM parameter derivation (e.g.,        pSelComp[ ]).    -   p. The above methods may be also applicable to the local        illumination compensation (LIC) process wherein according to the        number of available neighboring samples, LIC may be disabled.        -   i. Alternatively, according to the number of available            neighboring samples, LIC may be enabled but with specific            linear model parameters (e.g., a=1, b=0) regardless values            of the neighboring samples.

FIG. 14 shows a flowchart of an exemplary method for cross-componentprediction. The method 1400 includes, at step 1410, receiving abitstream representation of a current block of video data comprising aluma component and a chroma component.

In some embodiments, step 1410 includes receiving the bitstreamrepresentation from a memory location or buffer in a video encoder ordecoder. In other embodiments, step 1410 includes receiving thebitstream representation over a wireless or wired channel at a videodecoder. In yet other embodiments, step 1410 include receiving thebitstream representation from a different module, unit or processor,which may implement one or more methods or algorithms as described in,but not limited to, the embodiments in the present document.

The method 1400 includes, at step 1420, determining parameters of alinear model based on a first set of samples that are generated bydown-sampling a second set of samples of the luma component. In someembodiments, the second set of samples are positioned inside the currentblock, and determining the parameters is further based on a third set ofsamples that are generated by down-sampling a fourth set of samples thatare positioned outside the current block.

In some embodiments, and in the context of Example 2 in Section 4, thedown-sampling the second set of samples is based on a firstdown-sampling method, and the down-sampling the fourth set of samples isbased on a second down-sampling method that is different from the firstdown-sampling method.

In some embodiments, the fourth set of samples comprises neighboringluma samples of the current block. In other embodiments, the fourth setof samples comprises non-adjacent luma samples relative to the currentblock. In some embodiments, the second and/or fourth set of samples isselected based on dimensions of the current block.

The method 1400 includes, at step 1430, processing, based on theparameters of the linear model, the bitstream representation to generatethe current block.

In some embodiments, the method 1400 may further include the step ofpredicting a fifth set of samples of the chroma component based on theparameters of the linear model and the first set of samples, and theprocessing is further based on the fifth set of samples.

In some embodiments, and in the context of Example 4 in Section 4, themethod 1400 may further include the steps of predicting a fifth set ofsamples of the chroma component based on the parameters of the linearmodel and the first set of samples, and refining the fifth set ofsamples to generate a sixth set of samples. In an example, theprocessing may be further based on the sixth set of samples.

In some embodiments, the refining step described above may includeapplying a linear function to a set of neighboring reconstructed chromasamples and the fifth set of samples to generate the sixth set ofsamples. In one example, the set of neighboring reconstructed chromasamples are based on samples to a left of the current block. In anotherexample, the set of neighboring reconstructed chroma samples are furtherbased on samples above the current block.

In some embodiments, and in the context of Example 2 in Section 4, thedown-sampling the second set of samples is based on a down-samplingmethod that is selected based on a position of the first set of samplesin the current block, and the first set of samples if part of a 2×2block of luma samples (e.g., as shown in FIG. 4). In one example, thefirst set of samples comprises a lower left sample of the block of lumasamples. In another example, the first set of samples comprises an upperright sample or a lower right sample of the block of luma samples.

In some embodiments, and in the context of Example 5 in Section 4, aheight of the current block is greater than a first threshold, and awidth of the current block is greater than a second threshold. In someembodiments, a sum of a height of the current block and a width of thecurrent block is greater than a first threshold. In some embodiments, amultiplication of a height of the current block and a width of thecurrent block is greater than a first threshold. In some embodiments, aheight of the current block is greater than a first threshold or a widthof the current block is greater than a second threshold. In theseembodiments, the first threshold and/or the second threshold may besignaled in a Sequence Parameter Set (SPS), a Picture Parameter Set(PPS), a slice header, a picture header, a coding tree unit (CTU), acoding unit (CU), or a group of CTUs.

In some embodiments, and in the context of Example 6 in Section 4,processing the bitstream representation is further based on a positionof the current block in a current CTU. In an example, the position ofthe current block is at a top boundary of the current CTU. In anotherexample, the position of the current block is at a left boundary of thecurrent CTU.

Some additional techniques may be described using the followingclause-based description.

1. A method of video coding, comprising: using, during a conversionbetween a current block of video and a bitstream representation of thecurrent block, parameters of a linear model based on a first set ofsamples that are generated by down-sampling a second set of samples ofthe luma component; and performing, based on the parameters of thelinear model, the conversion between the bitstream representation andthe current block.

2. The method of clause 1, wherein a number of down-sampled luma samplesin the second set from above or left of the current block are dependenton a dimension of the current block.

3. The method of clause 2, wherein the current block is a chroma blockhaving width W and height H samples, where W and H are integers, andwhere W luma samples above the current block are down-sampled and H lumasamples left to the current block are down-sampled for W equal to H.

4. The method of clause 1, wherein two different downsampling schemesare used for cases when the current block is square and the currentblock is rectangular.

5. The method of clause 4, wherein the current block has a width W and aheight H samples, where W and H are integers, and wherein W>H, andwherein H leftmost above neighboring samples and H left neighboringsamples are used for a training process.

6. The method of clause 4, wherein the current block has a width W and aheight H samples, where W and H are integers, and wherein W>H, andwherein H rightmost above neighboring samples and H left neighboringsamples are involved in the training process.

In the above clauses the conversion may include video decoding ordecompression in which pixel values of the current block are generatedfrom its bitstream representation. In the above clauses, the conversionmay include video encoding or compression operation in which thebitstream representation is generated from the current block.

In some embodiments, a method of video processing includes performing aconversion between a current video block and a bitstream representationof the current video block using a cross-component linear model in whichmultiple lines of luma samples, at least one being non-adjacent to thecurrent video block are used for the conversion using thecross-component linear model.

Some Examples of Technical Solutions and Embodiments

Some solutions based on the disclosed techniques may be as follows.

1. A method for video coding, comprising: receiving a bitstreamrepresentation of a current block of video data comprising a lumacomponent and a chroma component; determining parameters of a linearmodel based on a first set of samples that are generated bydown-sampling a second set of samples of the luma component; andprocessing, based on the parameters of the linear model, the bitstreamrepresentation to generate the current block.

2. The method of solution 1, wherein the second set of samples arepositioned inside the current block, and wherein the determining theparameters is further based on a third set of samples that are generatedby down-sampling a fourth set of samples that are positioned outside thecurrent block.

3. The method of solution 2, wherein the down-sampling the second set ofsamples is based on a first down-sampling method, and wherein thedown-sampling the fourth set of samples is based on a seconddown-sampling method.

4. The method of solution 2 or 3, wherein the fourth set of samplescomprises neighboring luma samples of the current block.

5. The method of solution 2 or 3, wherein the fourth set of samplescomprises non-adjacent luma samples relative to the current block.

6. The method of solution 2, wherein the fourth set of samples isselected based on dimensions of the current block.

7. The method of solution 1, wherein the down-sampling is based on afiltering operation.

8. The method of solution 1, wherein the second set of samples isselected based on dimensions of the current block.

9. The method of any of solutions 1 to 8, further comprising: predictinga fifth set of samples of the chroma component based on the parametersof the linear model and the first set of samples. The processing isfurther based on the fifth set of samples.

10. The method of any of solutions 1 to 8, further comprising:predicting a fifth set of samples of the chroma component based on theparameters of the linear model and the first set of samples; andrefining the fifth set of samples to generate a sixth set of samples,wherein the processing is further based on the sixth set of samples.

11. The method of solution 10, wherein refining the fifth set of samplescomprises: applying a linear function to a set of neighboringreconstructed chroma samples and the fifth set of samples to generatethe sixth set of samples.

12. The method of solution 11, wherein the set of neighboringreconstructed chroma samples are based on samples to a left of thecurrent block.

13. The method of solution 12, wherein the set of neighboringreconstructed chroma samples are further based on samples above thecurrent block.

14. The method of solution 1, wherein the down-sampling the second setof samples is based on a down-sampling method that is selected based ona position of the first set of samples in the current block, and whereina 2×2 block of luma samples comprises the first set of samples.

15. The method of solution 14, wherein the first set of samplescomprises a lower left sample of the block of luma samples.

16. The method of solution 14, wherein the first set of samplescomprises an upper right sample or a lower right sample of the block ofluma samples.

17. The method of solution 1, wherein the first set of samples comprisesW samples, and wherein W is a width of the chroma component.

18. The method of solution 1, wherein the first set of samples comprisesH samples, wherein the second set of samples are left adjacent lumasamples relative to the current block, and wherein H is a height of thechroma component.

19. The method of solution 1, wherein a height of the current block isgreater than a first threshold, and wherein a width of the current blockis greater than a second threshold.

20. The method of solution 1 wherein a sum of a height of the currentblock and a width of the current block is greater than a firstthreshold.

21. The method of solution 1 wherein a multiplication of a height of thecurrent block and a width of the current block is greater than a firstthreshold.

22. The method of solution 1, wherein a height of the current block isgreater than a first threshold or a width of the current block isgreater than a second threshold.

23. The method of any of solutions 19 to 22, wherein the first thresholdis signaled in a Sequence Parameter Set (SPS), a Picture Parameter Set(PPS), a slice header, a picture header, a coding tree unit (CTU), acoding unit (CU), or a group of CTUs.

24. The method of solution 1, wherein the processing the bitstreamrepresentation is further based on a position of the current block in acurrent coding tree unit (CTU).

25. The method of solution 24, wherein the position of the current blockis at a top boundary of the current CTU.

26. The method of solution 24, wherein the position of the current blockis at a left boundary of the current CTU.

27. A method of video coding, comprising: using, during a conversionbetween a current block of video and a bitstream representation of thecurrent block, parameters of a linear model based on a first set ofsamples that are generated by down-sampling a second set of samples ofthe luma component; and performing, based on the parameters of thelinear model, the conversion between the bitstream representation andthe current block.

28. The method of solution 27, wherein a number of down-sampled lumasamples in the second set from above or left of the current block aredependent on a dimension of the current block.

29. The method of solution 28, wherein the current block is a chromablock having width W and height H samples, where W and H are integers,and where W luma samples above the current block are down-sampled and Hluma samples left to the current block are down-sampled for W equal toH.

30. The method of solution 27, wherein two different downsamplingschemes are used for cases when the current block is square and thecurrent block is rectangular.

31. The method of solution 30, wherein the current block has a width Wand a height H samples, where W and H are integers, and wherein W>H, andwherein H leftmost above neighboring samples and H left neighboringsamples are used for a training process.

32. The method of solution 30, wherein the current block has a width Wand a height H samples, where W and H are integers, and wherein W>H, andwherein H rightmost above neighboring samples and H left neighboringsamples are involved in the training process.

33. A method of video processing, comprising: performing a conversionbetween a current video block and a bitstream representation of thecurrent video block using a cross-component linear model in whichmultiple lines of luma samples, at least one being non-adjacent to thecurrent video block are used for the conversion using thecross-component linear model.

34. The method of solution 33, wherein the conversion uses a crosscomponent linear model mode comprising a multi-directional linear model.

35. The method of any of solutions 33-34, wherein the at least one ofthe multiple lines excludes lines satisfying an exclusion criterion orincludes lines satisfying an inclusion criterion.

36. The method of solution 35, wherein the exclusion criterion comprisesexcluding luma samples not used for directional intra prediction andmultiple reference line intra prediction.

37. The method of solution 35, wherein the inclusion criterion includestwo lines above the current video block.

38. An apparatus in a video system comprising a processor and anon-transitory memory with instructions thereon, wherein theinstructions upon execution by the processor, cause the processor toimplement the method in any one of solutions 1 to 37.

39. A computer program product stored on a non-transitory computerreadable media, the computer program product including program code forcarrying out the method in any one of solutions 1 to 37.

4 ADDITIONAL EMBODIMENTS FOR CROSS-COMPONENT PREDICTION Embodiment 1

In some embodiments, luma samples inside the current block aredown-sampled in the same way as in JEM.

In some embodiments, luma samples outside the current block and above tothe current block are down-sampled to position C in FIG. 4 with afilter. Suppose the luma samples above adjacent to the current block aredenoted as a[i], then d[i]=(a[2i−1]+2*a[2i]+a[2i+1]+2)>>2, where d[i]represents the down-sampled luma samples. If the sample a[2i−1] isunavailable, d[i]=(3*a[2i]+a[2i+1]+2)>>2.

In some embodiments, luma samples outside the current block and left tothe current block are down-sampled to the half position between B and D,as shown in FIG. 4. Suppose the luma samples left adjacent to thecurrent block are denoted as a[j], then d[j]=(a[2j]+a[2j+1]+1)>>1, whered[j] represents the down-sampled luma samples.

In some embodiments, W luma luma down-sampled samples from the aboveadjacent luma samples and H luma down-sampled samples from the leftadjacent luma samples are generated, where W and H are the width andheight of the current chroma block as shown in FIG. 13A.

In some embodiments, 2W luma luma down-sampled samples from the aboveadjacent luma samples and 2H luma down-sampled samples from the leftadjacent luma samples are generated, where W and H are the width andheight of the current chroma block as shown in FIG. 13B.

To constrain the neighboring luma samples required by the trainingprocess in a single line, down-sampling filters with less taps areapplied as shown in FIG. 3A:

For neighboring luma samples above:

Rec′ _(L)[x,y]=(2×Rec _(L)[2x,2y+1]+Rec _(L)[2x−1,2y+1]+Rec_(L)[2x+1,2y+1]+2)>>2.  (2)

-   -   For neighboring luma samples left:

Rec′ _(L)[x,y]=(Rec _(L)[2x+1,2y]+Rec _(L)[2x+1,2y+1]+1)>>1.  (3)

The luma samples inside the block are still down-sampled with thesix-tap filter.

Two solutions are provided in this contribution with usage of differentsets of neighboring luma samples. Suppose the width and height of oneblock is denoted as W and H, respectively. In solution #1, W aboveneighboring samples and H left neighboring samples are involved in thetraining process in FIG. 17A. In solution #2, 2W above neighboringsamples and 2H left neighboring samples are involved as shown in FIG.17B. It should be noted that the extended neighboring samples insolution #2 have already been used by wide angle intra-prediction.

In addition, solution #3 as shown in FIG. 18A-18C is provided based onsolution #2. 2W above neighboring samples are involved if W<=H;Otherwise, W above neighboring samples are involved. And 2H leftneighboring samples are involved if H<=W; Otherwise, H left neighboringsamples are involved.

As a further investigation, solution #1A, #2A and #3A are provided,which apply the same methods of solution #1, solution #2 and solution#3, respectively, but only on the above neighboring samples. In solution#1A, #2A and #3A, the left neighboring samples are down-sampled as inVTM-2.0, i.e., H left neighboring luma samples are down-sampled by the6-tap filter.

Embodiment #2

An embodiment on CCLM sample number constrains in Working Draft (examplechanges are marked—deletions are includes into double parentheses {{ }}.

Specification of INTRA_LT_CCLM, INTRA_L_CCLM and INTRA_T_CCLM IntraPrediction Mode

Inputs to this process are:

-   -   . . .        Output of this process are predicted samples predSamples[x][y],        with        x=0 . . . nTbW−1, y=0 . . . nTbH−1.        The current luma location (xTbY, yTbY) is derived as follows:

(xTbY,yTbY)=(xTbC<<1,yTbC<<1  (8-156)

The variables availL, availT and availTL are derived as follows:

-   -   . . .    -   The number of available top-right neighboring chroma samples        numTopRight is derived as follows:        -   The variable numTopRight is set equal to 0 and availTR is            set equal to TRUE.        -   When predModeIntra is equal to INTRA_T_CCLM, the following            applies for x=nTbW . . . 2*nTbW−1 until availTR is equal to            FALSE or x is equal to 2*nTbW−1:            -   . . .    -   The number of available left-below neighboring chroma samples        numLeftBelow is derived as follows:        -   . . .            The number of available neighboring chroma samples on the            top and top-right numTopSamp and the number of available            neighboring chroma samples on the left and left-below            nLeftSamp are derived as follows:    -   If predModeIntra is equal to INTRA_LT_CCLM, the following        applies:

numSampT=availT?nTbW:0  (8-157)

numSampL=availL?nTbH:0  (8-158)

-   -   Otherwise, the following applies:

numSampT=(availT &&predModeIntra==INTRA_T_CCLM)?(nTbW+Min(numTopRight,nTbH)):0  (8-159)

numSampL=(availL &&predModeIntra==INTRA_L_CCLM)?(nTbH+Min(numLeftBelow,nTbW)):0  (8-160)

The variable bCTUboundary is derived as follows:

bCTUboundary=(yTbC &(1<<(Ctb Log 2SizeY−1)−1)==0)?TRUE:FALSE.  (8-161)

The variable cntN and array pickPosN with N being replaced by L and T,are derived as follows:

-   -   The variable numIs4N is set equal to ((availT && availL &&        predModeIntra==INTRA_LT_CCLM) ? 0:1).    -   The variable startPosN is set equal to numSampN>>(2+numIs4N).    -   The variable pickStepN is set equal to Max(1,        numSampN>>(1+numIs4N)).    -   If availN is equal to TRUE and predModeIntra is equal to        INTRA_LT_CCLM or INTRA_N_CCLM, the following assignments are        made:        -   cntN is set equal to Min(numSampN, (1+numIs4N)<<1)        -   pickPosN[pos] is set equal to (startPosN+pos*pickStepN),            with pos=0 . . . cntN−1.    -   Otherwise, cntN is set equal to 0.        The prediction samples predSamples[x][y] with x=0 . . . nTbW−1,        y=0 . . . nTbH−1 are derived as follows:    -   If both numSampL and numSampT are equal to 0, the following        applies:

predSamples[x][y]=1<<(BitDepth_(C)−1)  (8-162)

-   -   Otherwise, the following ordered steps apply:        -   1. The collocated luma samples pY[x][y] with x=0 . . .            nTbW*2−1, y=0 . . . nTbH*2−1 are set equal to the            reconstructed luma samples prior to the deblocking filter            process at the locations (xTbY+x, yTbY+y).        -   2. The neighboring luma samples samples pY[x][y] are derived            as follows:            -   When numSampL is greater than 0, the neighboring left                luma samples pY[x][y] with x=−1 . . . −3, y=0 . . .                2*numSampL−1, are set equal to the reconstructed luma                samples prior to the deblocking filter process at the                locations (xTbY+x, yTbY+y).            -   When numSampT is greater than 0, the neighboring top                luma samples pY[x][y] with x=0 . . . 2*numSampT−1, y=−1,                −2, are set equal to the reconstructed luma samples                prior to the deblocking filter process at the locations                (xTbY+x, yTbY+y).            -   When availTL is equal to TRUE, the neighboring top-left                luma samples pY[x][y] with x=−1, y=−1, −2, are set equal                to the reconstructed luma samples prior to the                deblocking filter process at the locations (xTbY+x,                yTbY+y).        -   3. The down-sampled collocated luma samples pDsY[x][y] with            x=0 . . . nTbW−1, y=0 . . . nTbH−1 are derived as follows:            -   . . .        -   4. When numSampL is greater than 0, the selected neighboring            left chroma samples pSelC[idx] are set equal to            p[−1][pickPosL[idx]] with idx=0 . . . cntL−1, and the            selected down-sampled neighboring left luma samples            pSelDsY[idx] with idx=0 . . . cntL−1 are derived as follows:            -   The variable y is set equal to pickPosL[idx].            -   If sps_cclm_colocated_chroma_flag is equal to 1, the                following applies:                -   . . .            -   Otherwise, the following applies:

pSelDsY[idx]=(pY[−1][2*y]+pY[−1][2*y+1]+2*pY[−2][2*y]+2*pY[−2][2*y+1]+pY[−−3][2*y]+pY[−−3][2*y+1]+4)>>3  (8-178)

-   -   -   5. When numSampT is greater than 0, the selected neighboring            top chroma samples pSelC[idx] are set equal to            p[pickPosT[idx−cntL]][−1] with idx=cntL . . . cntL+cntT−1,            and the down-sampled neighboring top luma samples            pSelDsY[idx] with idx=0 . . . cntL+cntT−1 are specified as            follows:            -   The variable x is set equal to pickPosT[idx−cntL].            -   If sps_cclm_colocated_chroma_flag is equal to 1, the                following applies:        -   6. When cntT+cntL is not {{equal to 0}} smaller than            Threshold1, the variables minY, maxY, minC and maxC are            derived as follows:            -   {{When cntT+cntL is equal to 2, set pSelComp[3] equal to                pSelComp[0], pSelComp[2] equal to pSelComp[1],                pSelComp[0] equal to pSelComp[1], and pSelComp[1] equal                to pSelComp[3], with Comp being replaced by DsY and C .                . . }}            -   The arrays minGrpIdx and maxGrpIdx are set as follows:                -   minGrpIdx[0]=0.                -   minGrpIdx[1]=2.                -   maxGrpIdx[0]=1.                -   maxGrpIdx[1]=3.            -   When pSelDsY[minGrpIdx[0]] is greater than                pSelDsY[minGrpIdx[1]], minGrpIdx[0] and minGrpIdx[1] are                swapped as (minGrpIdx[0],                minGrpIdx[1])=Swap(minGrpIdx[0], minGrpIdx[1]).            -   When pSelDsY[maxGrpIdx[0]] is greater than                pSelDsY[maxGrpIdx[1]], maxGrpIdx[0] and maxGrpIdx[1] are                swapped as (maxGrpIdx[0],                maxGrpIdx[1])=Swap(maxGrpIdx[0], maxGrpIdx[1]).            -   When pSelDsY[minGrpIdx[0]] is greater than                pSelDsY[maxGrpIdx[1]], arrays minGrpIdx and maxGrpIdx                are swapped as (minGrpIdx, maxGrpIdx)=Swap(minGrpIdx,                maxGrpIdx).            -   When pSelDsY[minGrpIdx[1]] is greater than                pSelDsY[maxGrpIdx[0]], minGrpIdx[1] and maxGrpIdx[0] are                swapped as (minGrpIdx[1],                maxGrpIdx[0])=Swap(minGrpIdx[1], maxGrpIdx[0]).

maxY=(pSelDsY[maxGrpIdx[0]]+pSelDsY[maxGrpIdx[1]]+1)>>1.

maxC=(pSelC[maxGrpIdx[0]]+pSelC[maxGrpIdx[1]]+1)>>1.

minY=(pSelDsY[minGrpIdx[0]]+pSelDsY[minGrpIdx[1]]+1)>>1.

minC=(pSelC[minGrpIdx[0]]+pSelC[minGrpIdx[1]]+1)>>1.

-   -   -   7. The variables a, b, and k are derived as follows:            -   If {{numSampL is equal to 0, and numSampT is equal to                0}} cntT+cntL is smaller than Threshold), the following                applies:

k=0  (8-208)

a=0  (8-209)

b=1<<(BitDepth_(C)−1)  (8-210)

-   -   -   -   Otherwise, the following applies:

        -   8. The prediction samples predSamples[x][y] with x=0 . . .            nTbW−1, y=0 . . . nTbH−1 are derived as follows:

predSamples[x][y]=Clip1C(pDsY[x][y]*a)>>k)+b)  (8-225)

In one example, Threshold 1 is set to 4.

In some embodiments, the following technical solutions may beimplemented:

1. A method of video processing (e.g., method 2100 depicted in FIG. 21),comprising: determining (2102), for a conversion between a video and acoded representation of the video, at least using down-sampled lumasamples outside a luma block, a linear model used for predicting a firstchroma block of the video that is co-located with the luma block,determining (2104), using down-sampled luma samples inside the lumablock and the linear model, predicted values of samples of the firstchroma block, and performing (2106) the conversion based on thepredicted values of samples of the first chroma block; wherein thedown-sampled luma samples outside the luma block are obtained byapplying an outside filter to luma samples outside the luma block; andwherein the down-sampled luma samples inside the luma block are obtainedby applying an inside filter to luma samples inside the luma block.

2. The method of solution 1, wherein the down-sampled luma samplesinside the luma block are at positions corresponding to chroma samplepositions of the first chroma block.

3. The method of any of solutions 1-2, wherein the down-sampled lumasamples inside the luma block and the down-sampled luma samples outsidethe luma block are further used for a second prediction of the firstchroma block.

4. The method of any of solutions 1-3, wherein the inside filtercorresponds to a legacy filter.

5. The method of any of solutions 1-3, wherein the outside filter isdifferent from the inside filter.

6. The method of any of solutions 1 to 5, wherein the luma samplesoutside the luma block include neighboring luma samples or non-adjacentluma samples.

7. The method of solution 6, wherein the down-sampled luma samplesoutside the luma block are also used to determine the predicted valuesof samples of the first chroma block.

8. The method of any of solutions 1 to 7 wherein filter taps or filtercoefficients of the outside filter are dependent on whether the outsidefilter is applied to left or above the luma block.

9. The method of any of solutions 1-8, wherein a length of the insidefilter or a length of the outside filter are dependent on a shape or asize of the first chroma block or the luma block.

10. The method of any of solutions 1-9, wherein positions of the lumasamples outside the luma block or inside the luma block are a functionof a size or a shape of the first chroma block or the luma block.

11. The method of any of solution 1-10, wherein the determining samplesof the first chroma block comprises predicting samples of the firstchroma block using a refined prediction block that is a refinement of anintermediate prediction block generated by applying the linear model tothe down-sampled luma samples inside the luma block.

12. The method of solution 11, wherein the refinement of theintermediate prediction block uses reconstructed chroma samples that areneighboring to the first chroma block.

13. The method of solution 11, wherein the refinement of theintermediate prediction block uses reconstructed chroma samples that arenon-adjacent to the first chroma block.

14. The method of any of solutions 11-13, wherein the refined predictionblock is determined as a linear function of the intermediate predictionblock and neighboring or non-adjacent reconstructed chroma samples.

15. The method of any of solutions 11-14, wherein the refinementincludes copying samples of the intermediate prediction block as therefined prediction block in certain positions of the first chroma block.

16. The method of solution 11, wherein the refined prediction block isgenerated from the intermediate prediction block and another chromaprediction block that is generated from previously reconstructed chromablocks.

17. The method of solution 16, wherein the another chroma predictionblock is an intra predicted chroma block.

18. The method of any of solutions 16-17, wherein the samples of thefirst chroma block are determined as a linear weighting of theintermediate prediction block and the another chroma prediction block.

19. The method of solution 18, wherein, at some pixel positions of thefirst chroma block, the another chroma prediction block is given 0%weight and the intermediate prediction block is given 100% weight.

20. The method of solutions 17-19, wherein the intra predicted chromablock uses an intra prediction mode of the luma block.

21. The method of solution 21, wherein the some pixel positions includestop k rows of the first chroma block or left m columns of the firstchroma block, where k and m are integers.

22. The method of any of solutions 1 to 21, wherein the conversionfurther includes performing boundary filtering to the luma block and/orthe first chroma block.

23. The method of solution 22, wherein the boundary filtering is appliedonly in case that above or left neighboring samples or both above andleft samples are available.

24. The method of solution 12, wherein the reconstructed chroma samplesthat are neighboring to the first chroma block comprises above, left orabove and left chroma samples.

25. The method of solution 24, wherein the above chroma samples arerepresented as a[−1][j] and wherein a sample in ith row and jth columnof the intermediate prediction block is a[i][j], then a sample at(i,j)th position in the first chroma block is computed as a′[i][j]=(w1*a[i][j]+w2*a[−1][[j]+2N−1)>>N, where w1+w2=2N where N is aninteger.

26. The method of solution 22, wherein the boundary filtering is appliedonly for rows i<=K, where K is an integer equal to zero or 1.

27. The method of solutions 25-26, wherein K=0, w1=3 and w2=1.

28. The method of solution 25, wherein w1 and w2 are a function of rowindex i.

29. The method of solution 28, wherein K=1, w1=w2=1 for i=0 and K=1,w1=3, w2=1 for i=1.

30. The method of solution 40, wherein the left chroma samples arerepresented as a[i][−1] and wherein a sample in ith row and jth columnof the intermediate prediction block is a[i][j], then a sample at(i,j)th position in the first chroma block is computed as a′[i][j]=(w1*a[i][j]+w2*a[i][[−1]+2N−1) >>N, where w1+w2=2N where N is aninteger.

31. The method of solution 22, wherein the boundary filtering is appliedonly for columns j<=K, where K is an integer equal to zero or 1.

32. The method of solutions 25-26, wherein K=0, w1=3 and w2=1.

33. The method of solution 25, wherein w1 and w2 are a function ofcolumn index j.

34. The method of solution 28, wherein K=1, w1=w2=1 for j=0 and K=1,w1=3, w2=1 for j=1.

35. The method of solution 24, wherein the left chroma samples arerepresented as a[i][−1], above samples are represented as a[−1][j] andwherein a sample in ith row and jth column of the intermediateprediction block is a[i][j], then a sample at (i,j)th position in thefirst chroma block is computed as a′[i][j]=(w1*a[i][j]+w2*a[i][[−1]+w3*a[−1][j]+2N−1)>>N, where w1+w2+w3=2Nwhere N is an integer.

36. The method of solution 22, wherein the boundary filtering is appliedonly for columns i<=K, columns j<=P, where K and P an integer equal tozero or 1.

37. The method of solutions 25-27, wherein K=0, w1=3 and w2=1.

38. The method of solution 25, wherein w1 and w2 are a function ofcolumn index j.

39. The method of solution 28, wherein boundary filtering is applied toa[0][0] with w1=2 and w2=w3=1.

40. A method of video processing (e.g., method 2200 depicted in FIG.22), comprising: determining (2202), for a conversion between a videoand a coded representation of the video, one or more cross-componentlinear models (CCLMs) used for prediction of a first component blockfrom a second component block during the conversion based on a rule thatuses a number of available neighboring samples or a size of the firstcomponent block; and performing (2204) the conversion using thecross-component linear model, wherein the cross-component linear modelis one of: a CCLM derived from above and/or right-above neighboringvalues of the first component block (CCLM-A); a CCLM derived from leftand/or left-below neighboring values of the first component block(CCLM-T); or a CCLM derived from only left and top neighboring values ofthe first component block (CCLM-TL).

41. The method of solution 40, wherein the first component blockcorresponds to a chroma block.

42. The method of any of solutions 40-41, wherein the second componentblock corresponds to a luma block.

43. The method of any of solutions 40-42, wherein, in case that the ruledisables use of a CCLM, then the coded representation omits signalingthe CCLM using a syntax element.

44. The method of any of solutions 40-42, wherein, in case that the ruledisables use of a CCLM, then the coded representation signals via asyntax element in the coded representation that the CCLM is disabled.

45. The method of any of solutions 40-42, wherein, in case that the ruledisables use of a CCLM, and the coded representation signals includes asyntax element for the CCLM, wherein a decoder is expected to ignore thesyntax element.

46. The method of any of solutions 40-45, wherein the availableneighboring samples comprising chroma neighboring samples.

47. The method of any of solutions 40-46, wherein the availableneighboring samples comprising luma neighboring samples.

48. The method of any of solutions 40-46, wherein the availableneighboring samples comprising down-sampled luma neighboring samples.

49. The method of any of solutions 40-48, wherein the codedrepresentation signals a specific CCLM, wherein the specific CCLM uses alinear model in which a slope alpha is zero and an intercept beta is afixed number.

50. The method of solution 49, wherein the fixed number is a function ofbit depth.

51. The method of any of solutions 40-50, wherein the rule disables useof CCLM or CCLM-A or CCLM-T due to a number of available neighboringsamples is less than a threshold.

52. The method of any of solutions 40-50, wherein the rule disables useof CCLM due to a width of the chroma block being two or unavailabilityof left neighboring samples.

53. The method of any of solutions 40-50, wherein the rule disables useof CCLM due to a width of the chroma block being two or unavailabilityof above neighboring samples.

54. The method of any of solutions 40-50, wherein the rule disables useof CCLM-T due to a width of the chroma block being two.

55. The method of any of solutions 40-50, wherein the rule disables useof CCLM-T due to a width of the chroma block being two and a right-aboveneighboring block is unavailable.

56. The method of any of solutions 40-50, wherein the rule disables useof CCLM-L due to a height of the chroma block being two.

57. The method of any of solutions 40-50, wherein the rule disables useof CCLM-L due to a height of the chroma block being two and aright-above neighboring block is unavailable.

58. The method of any of solutions 40-57, wherein the rule specifies todisable CCLM-L due to CCLM not being available.

59. The method of any of solutions 40-57, wherein the rule specifies todisable CCLM-T due to CCLM not being available.

60. The method of any of solutions 40-59, wherein the availableneighboring samples are left or above samples with respect one or morereference lines selected for the chroma block.

61. The method of any of solutions 40-59, wherein the availableneighboring samples are left or above samples with respect to one ormore reference lines selected for the chroma block and a rule ofparameter derivation for the CCLM.

62. The method of any of solutions 40-61, wherein the conversion furtherincludes decoding to selectively disabling or constraining localillumination compensation during the conversion of the first componentblock based on the number of available neighboring samples.

63. The method of any of solutions 40-61, wherein the selectivelyconstraining the local illumination compensation includes forcingspecific linear parameter values for use with the CCLM.

64. The method of any of solutions 1-63, wherein the conversioncomprises generating the video from the coded representation.

65. The method of any of solutions 1-63, wherein the conversioncomprises generating the coded representation from the video.

66. A video processing apparatus comprising a processor configured toimplement a method recited in any one or more of solutions 1-65.

67. A computer-readable program medium having code stored thereupon, thecode, when executed by a processor, causing the processor to implement amethod recited in any one or more of solutions 1-65.

In addition to the above solutions, in some embodiments, the followingsolutions may be implemented.

1. A method of video processing (e.g., method 1900 depicted in FIG. 19),comprising: generating (1902) downsampled outside luma samplescorresponding to chroma samples outside a chroma block of a video usinga first downsampling scheme; generating (1904) downsampled inside lumasamples with corresponding to chroma samples inside the chroma blockusing a second downsampling scheme; at least using (1906) thedownsampled outside luma samples to derive a linear model forcross-component prediction; determining (1908) predicted samples for thechroma block using the linear model and the downsampled inside lumasamples; and performing (1910) a conversion between the video and acoded representation of the video using the predicted samples for thechroma block.

2. The method of solution 1, wherein the first downsampling schemecorresponds to downsampling outside above luma samples to lower leftand/or lower right positions.

3. The method of solutions 1 or 2, wherein the first downsampling schemecorresponds to downsampling outside luma samples to lower leftpositions.

4. The method of solution 3, wherein the first downsampling schemecalculates downsampled luma samples d[i] from above adjacent lumasamples a[i] as

d[i]=(a[2i−1]+2*a[2i]+a[2i+1]+2)>>2 in a case that all a samples areavailable, or

-   -   d[i]=(3*a[2i]+a[2i+1]+2)>>2 in a case that a[2i−1] is        unavailable, or    -   d[i]=(3*a[2i]+a[2i−1]+2)>>2 in a case that a[2i+1] is        unavailable.

5. The method of solution 3, wherein the first downsampling schemecalculates downsampled luma samples d[i] from above adjacent lumasamples a[i] as

d[i]=(a[2i−1]+2*a[2i]+a[2i+1]+offset0)>>2, for i>0 and one of

-   -   d[i]=(3*a[2i]+a[2i+1]+offset1)>>2, or    -   d[i]=a[2i], or    -   d[i]=(a[2i]+a[2i+1]+offset2)>>1, for i=0, where offset0, offset1        and offset2 are fractions.

6. The method of solution 5, wherein offset0=offset1=2 and offset2=1.

7. The method of solution 1, wherein the first downsampling schemecorresponds to downsampling outside left luma samples to lower rightand/or upper right positions.

8. The method of solution 1, wherein the first downsampling schemecorresponds to downsampling outside left luma samples to halfway betweenlower right and upper right positions.

9. The method of solution 8, wherein a downsampled luma samples d[i] iscalculated as:

d[j]=(a[2j]+a[2j+1]+1)>>2.

10. The method of solution 1, wherein the chroma block has a width W anda height of H pixels, and wherein the first downsampling schemegenerates N*W luma samples, where N is an integer.

11. The method of solution 10, wherein N=2, and wherein the firstdownsampling scheme generates luma samples from above adjacent lumasamples.

12. The method of solution 1, wherein the chroma block has a width W anda height of H pixels, and wherein the first downsampling schemegenerates W+K luma downsampled samples from above adjacent luma samples,where K is a positive integer.

13. The method of solution 1, wherein the chroma block has a width W anda height of H pixels, and wherein the first downsampling schemegenerates W/N luma downsampled samples from above adjacent samples,where N is a positive integer.

14. The method of solution 1, wherein the chroma block has a width W anda height of H pixels, and wherein the first downsampling schemegenerates W*N luma downsampled samples from left adjacent luma samples,where N is a positive integer.

15. The method of solution 1, wherein the chroma block has a width W anda height of H pixels, and wherein the first downsampling schemegenerates W+K luma downsampled samples from left adjacent luma samples,where K is a positive integer.

16. The method of solution 1, wherein the chroma block has a width W anda height of H pixels, and wherein the first downsampling schemegenerates W/N luma downsampled samples from left adjacent luma samples,where N is a positive integer.

17. The method of solution 1, wherein the first downsampling scheme orthe second downsampling scheme is determined based on a position of achroma block of a video meeting a position criterion.

18. The method of any of solutions 1-16, wherein the position criterionspecifies to use the method for only video blocks at a top boundary of acoding tree unit of the video.

19. The method of any of solutions 1-16, wherein the position criterionspecifies to use the method for only video blocks at a left boundary ofa coding tree unit of the video.

20. The method of any of solutions 1-16, wherein the position criterionspecifies to use the method for only video blocks at a top boundary of acoding tree unit of the video or video blocks at a left boundary of acoding tree unit of the video.

21. The method of any of solutions 1-20, wherein the first downsamplingscheme uses only one above neighboring luma samples row to derive thedownsampled outside luma samples.

22. The method of solution 21, wherein the one above neighboring lumasamples row comprises above neighboring samples and above-rightneighboring samples.

23. A method for video processing (e.g., method 2000 depicted in FIG.20), comprising: determining (2002), for a conversion between a chromablock of a video and a coded representation of the video, a linearmodel; generating (2004) prediction values of the chroma block from aluma block that corresponds to the chroma block based on the linearmodel; and performing (2006) the conversion using the linear model;wherein the predicting the chroma block from the luma block includesdownsampling luma samples above the luma block by a first filteringmethod and dowsampling luma samples to left of the luma block by asecond filtering method, and wherein the linear model is determined atleast based on the downsampled luma samples.

24. The method of solution 23, wherein the first filtering method usesonly luma samples of a single above neighboring luma row during theconversion of the video.

25. The method of solution 23, wherein the first filtering method isdifferent than the second filtering method.

26. The method of any of solutions 23-25, wherein the first filteringmethod uses a horizontal three-tap filter.

27. The method of any of solutions 23-26, wherein the second filteringmethod uses a 2-d 6 tap filter.

28. The method of any of solutions 1-27, wherein the conversioncomprises generating the video from the coded representation.

29. The method of any of solutions 1-27, wherein the conversioncomprises generating the coded representation from the video.

30. A video processing apparatus comprising a processor configured toimplement a method recited in any one or more of solutions 1-29.

31. A computer-readable program medium having code stored thereupon, thecode, when executed by a processor, causing the processor to implement amethod recited in any one or more of solutions 1-29.

The various video processing solutions described herein may beimplemented during video encoding or compression, video decoding ordecompression, and video transcoding, or converting video representationfrom one coded format to another.

Furthermore, while reciting for the first chroma block belonging to afirst chroma component of the video, similar techniques may also be usedfor a second chroma block that belongs to a second chroma component ofthe video (e.g., Cr and Cb or U or V or another type of chromarepresentation).

In addition to the above solutions, in some embodiments, the followingsolutions may be implemented.

1. A method for video processing (e.g., method 2300 depicted in FIG.23), comprising: determining (2302), for a conversion between a videoand a bitstream representation of the video and based on a height (H) ora width (W) of a chroma block of the video, parameters of a linearmodel, wherein the parameters of the linear model are based ondownsampled luma samples corresponding to chroma samples outside thechroma block, and wherein H and W are positive integers; generating(2304) a set of samples of the chroma block based on the parameters ofthe linear model and a set of downsampled luma samples inside a lumablock corresponding to the chroma block; and performing (2306), based onthe generating, the conversion between the video and the bitstreamrepresentation.

2. The method of solution 1, further comprising: applying a firstdownsampling scheme to generate the downsampled luma samples outside theluma block.

3. The method of solution 2, further comprising: applying a seconddownsampling scheme, different from the first downsampling scheme, togenerate the downsampled samples inside the luma block.

4. The method of any of solutions 1 to 3, wherein W>T1 or H>T2, andwherein T1 and T2 are integers.

5. The method of solution 4, wherein T1=T2=4.

6. The method of solution 4, wherein T1=T2=2.

7. The method of any of solutions 1 to 3, wherein H+W>T1, and wherein T1is an integer.

8. The method of solution 7, wherein T1=6.

9. The method of any of solutions 1 to 3, wherein W×H>T1, and wherein T1is an integer.

10. The method of solution 9, wherein T1=16.

11. The method of any of solutions 4 to 10, wherein T1 is predefined orsignaled in a sequence parameter set (SPS), a sequence header, a pictureparameter set (PPS), a picture header, a video parameter set (VPS), aslice header, a coding tree unit (CTU), a coding unit (CU) or a group ofCTUs.

12. The method of any of solutions 1 to 11, wherein the linear modelcomprises a multiple model linear mode (MMLM) or a single model linearmodel (LM).

13. The method of solution 12, wherein the downsampled luma samplesoutside the luma block consists of either only left neighboring samplesor only above neighboring samples.

14. The method of solution 13, wherein the linear model using the onlyleft neighboring samples (Left-LM) is applied upon a determination thatH>T1, and wherein T1 is an integer.

15. The method of solution 13, wherein the linear model using the onlyabove neighboring samples (Above-LM) is applied upon a determinationthat W>T1, and wherein T1 is an integer.

16. The method of solution 14 or 15, wherein T1=4.

17. A method for video processing (e.g., method 2400 depicted in FIG.24), comprising: determining (2402), for a conversion between a videoand a bitstream representation of the video and based on a height (H) ora width (W) of a chroma block of the video, parameters of a linearmodel, wherein the parameters of the linear model are based on a firstset of luma samples that are generated by down-sampling a second set ofluma samples corresponding to chroma samples outside the chroma block,and wherein a number of the first set of luma samples is based on theheight or the width, and wherein H and W are positive integers;generating (2404) a set of samples of the chroma block based on theparameters of the linear model and a set of downsampled luma samplesinside a luma block corresponding to the chroma block; and performing(2406), based on the generating, the conversion between the video andthe bitstream representation.

18. The method of solution 17, wherein the first set of luma samplescomprises m×W samples above the luma block and m×H luma samples to aleft of the luma block upon a determination that W is equal to H, andwherein m is a positive integer.

19. The method of solution 17, wherein the first set of luma samplescomprises 2×W luma samples above the luma block and H luma samples to aleft of the luma block upon a determination that W≤H.

20. The method of solution 17, wherein the first set of luma samplescomprises 2×W luma samples above the luma block and H luma samples to aleft of the luma block upon a determination that W≥H.

21. The method of solution 17, wherein the first set of luma samplescomprises W luma samples above the luma block and 2×H luma samples to aleft of the luma block upon a determination that W≤H.

22. The method of solution 17, wherein the first set of luma samplescomprises W luma samples above the luma block and 2×H samples to a leftof the luma block upon a determination that W≥H.

23. The method of solution 17, wherein the first set of luma samplescomprises 2×W luma samples above the luma block upon a determinationthat an above neighboring block and an above-right neighboring block areboth available, otherwise the second set of luma samples comprises Wluma samples above the luma block.

24. The method of solution 17, wherein the first set of luma samplescomprises 2×H luma samples above the luma block upon a determinationthat a left neighboring block and a left-bottom neighboring block areboth available, otherwise the first set of luma samples comprises H lumasamples above the luma block.

25. The method of solution 17, wherein the first set of luma samplesconsist of samples above the current video block, and wherein theparameters of the linear model are further based on a third set of lumasamples that are generated by down-sampling a fourth set of luma samplesto a left of the luma block.

26. The method of solution 25, wherein the third set of luma samples isdecimated upon a determination that a number of the third set of lumasamples is greater than a number of the first set of luma samples.

27. The method of solution 25, wherein the first set of luma samples isdecimated upon a determination that a number of the first set of lumasamples is greater than a number of the third set of luma samples.

28. The method of solution 17, wherein a number of the first set of lumasamples is less than a predetermined value or in a range of values.

29. The method of solution 28, wherein the predetermined value is max(W,H).

30. The method of solution 28, wherein the range of values is frommin(W, H) to s×max(W, H), and wherein s is a positive integer.

31. The method of solution 30, wherein s=1 or s=2.

32. The method of solution 17, wherein the parameters of the linearmodel are further based on training samples of the luma blockcorresponding to the chroma block.

33. The method of solution 32, wherein the training samples comprise atop-most predetermined number of left neighboring samples and aleft-most predetermined number of above neighboring samples.

34. The method of solution 33, wherein the predetermined number ismin(W, H).

35. The method of solution 32, wherein the second set of luma samplesoutside the luma block comprise W above neighboring samples and H leftneighboring samples, and wherein the above neighboring samples arerepeated n times upon a determination that H=n×W.

36. The method of solution 32, wherein the second set of luma samplesoutside the luma block comprise W above neighboring samples and H leftneighboring samples, and wherein the left neighboring samples arerepeated n times upon a determination that W=n×H.

37. The method of any of solutions 1 to 36, wherein performing theconversion comprises generating the bitstream representation from thecurrent video block.

38. The method of any of solutions 1 to 36, wherein performing theconversion comprises generating the current video block from thebitstream representation.

5 EXAMPLE IMPLEMENTATIONS OF THE DISCLOSED TECHNOLOGY

FIG. 15 is a block diagram of a video processing apparatus 1500. Theapparatus 1500 may be used to implement one or more of the methodsdescribed herein. The apparatus 1500 may be embodied in a smartphone,tablet, computer, Internet of Things (IoT) receiver, and so on. Theapparatus 1500 may include one or more processors 1502, one or morememories 1504 and video processing hardware 1506. The processor(s) 1502may be configured to implement one or more methods (including, but notlimited to, method 1400) described in the present document. The memory(memories) 1504 may be used for storing data and code used forimplementing the methods and techniques described herein. The videoprocessing hardware 1506 may be used to implement, in hardwarecircuitry, some techniques described in the present document.

In some embodiments, the video coding methods may be implemented usingan apparatus that is implemented on a hardware platform as describedwith respect to FIG. 15.

FIG. 25 is a block diagram showing an example video processing system2500 in which various techniques disclosed herein may be implemented.Various implementations may include some or all of the components of thesystem 2500. The system 2500 may include input 2502 for receiving videocontent. The video content may be received in a raw or uncompressedformat, e.g., 8- or 10-bit multi-component pixel values, or may be in acompressed or encoded format. The input 2502 may represent a networkinterface, a peripheral bus interface, or a storage interface. Examplesof network interface include wired interfaces such as Ethernet, passiveoptical network (PON), etc. and wireless interfaces such as Wi-Fi orcellular interfaces.

The system 2500 may include a coding component 2504 that may implementthe various coding or encoding methods described in the presentdocument. The coding component 2504 may reduce the average bitrate ofvideo from the input 2502 to the output of the coding component 2504 toproduce a coded representation of the video. The coding techniques aretherefore sometimes called video compression or video transcodingtechniques. The output of the coding component 2504 may be eitherstored, or transmitted via a communication connected, as represented bythe component 2506. The stored or communicated bitstream (or coded)representation of the video received at the input 2502 may be used bythe component 2508 for generating pixel values or displayable video thatis sent to a display interface 2510. The process of generatinguser-viewable video from the bitstream representation is sometimescalled video decompression. Furthermore, while certain video processingoperations are referred to as “coding” operations or tools, it will beappreciated that the coding tools or operations are used at an encoderand corresponding decoding tools or operations that reverse the resultsof the coding will be performed by a decoder.

Examples of a peripheral bus interface or a display interface mayinclude universal serial bus (USB) or high definition multimediainterface (HDMI) or Displayport, and so on. Examples of storageinterfaces include SATA (serial advanced technology attachment), PCI,IDE interface, and the like. The techniques described in the presentdocument may be embodied in various electronic devices such as mobilephones, laptops, smartphones or other devices that are capable ofperforming digital data processing and/or video display.

From the foregoing, it will be appreciated that specific embodiments ofthe presently disclosed technology have been described herein forpurposes of illustration, but that various modifications may be madewithout deviating from the scope of the invention. Accordingly, thepresently disclosed technology is not limited except as by the appendedclaims.

Implementations of the subject matter and the functional operationsdescribed in this patent document can be implemented in various systems,digital electronic circuitry, or in computer software, firmware, orhardware, including the structures disclosed in this specification andtheir structural equivalents, or in combinations of one or more of them.Implementations of the subject matter described in this specificationcan be implemented as one or more computer program products, i.e., oneor more modules of computer program instructions encoded on a tangibleand non-transitory computer readable medium for execution by, or tocontrol the operation of, data processing apparatus. The computerreadable medium can be a machine-readable storage device, amachine-readable storage substrate, a memory device, a composition ofmatter effecting a machine-readable propagated signal, or a combinationof one or more of them. The term “data processing unit” or “dataprocessing apparatus” encompasses all apparatus, devices, and machinesfor processing data, including by way of example a programmableprocessor, a computer, or multiple processors or computers. Theapparatus can include, in addition to hardware, code that creates anexecution environment for the computer program in question, e.g., codethat constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, or a combination of one or moreof them.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, and it can bedeployed in any form, including as a stand-alone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment. A computer program does not necessarily correspond to afile in a file system. A program can be stored in a portion of a filethat holds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read only memory ora random access memory or both. The essential elements of a computer area processor for performing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g.,magnetic, magneto optical disks, or optical disks. However, a computerneed not have such devices. Computer readable media suitable for storingcomputer program instructions and data include all forms of nonvolatilememory, media and memory devices, including by way of examplesemiconductor memory devices, e.g., EPROM, EEPROM, and flash memorydevices. The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

It is intended that the specification, together with the drawings, beconsidered exemplary only, where exemplary means an example. As usedherein, the use of “or” is intended to include “and/or”, unless thecontext clearly indicates otherwise.

While this patent document contains many specifics, these should not beconstrued as limitations on the scope of any invention or of what may beclaimed, but rather as descriptions of features that may be specific toparticular embodiments of particular inventions. Certain features thatare described in this patent document in the context of separateembodiments can also be implemented in combination in a singleembodiment. Conversely, various features that are described in thecontext of a single embodiment can also be implemented in multipleembodiments separately or in any suitable subcombination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. Moreover, the separation of various system components in theembodiments described in this patent document should not be understoodas requiring such separation in all embodiments.

Only a few implementations and examples are described and otherimplementations, enhancements and variations can be made based on whatis described and illustrated in this patent document.

1. A method of processing video data, comprising: determining, for aconversion between a current video block of a video that is a chromablock and a bitstream of the video, a corresponding luma block for thecurrent video block, wherein a color format for the current video blockand the corresponding luma block is 4:2:0, and a cross component linearmode is applied for the current video block; generating down-sampledinside luma samples of the corresponding luma block; generatingdown-sampled above neighboring luma samples of the corresponding lumablock, wherein different down-sampled filtering schemes are used togenerate the down-sampled above neighboring luma samples based on aposition of the corresponding luma block, and wherein in response to theposition of the corresponding luma block meeting a first position rule,a first down-sampled filtering scheme is used to generate thedown-sampled above neighboring luma samples, and wherein the firstposition rule is that a top boundary of the corresponding luma block isoverlapping with a top boundary of a current luma coding tree block(CTB) including the corresponding luma block, and wherein only one aboveluma sample row and a horizontal 3-tap filter are used to generate thedown-sampled above neighboring luma samples in the first down-sampledfiltering scheme; deriving parameters of the cross-component linearmodel at least based on the down-sampled above neighboring luma samples;generating predicted chroma samples of the current video block based onthe parameters of the cross-component linear model and the down-sampledinside luma samples; and performing the conversion based on thepredicted chroma samples, wherein the only one above luma sample row isadjacent to the corresponding luma block; and wherein in response to theposition of the corresponding luma block not meeting the first positionrule, a second down-sampled filtering scheme is used to generate thedown-sampled above neighboring luma samples.
 2. The method of claim 1,wherein the only one above luma sample row comprises above adjacent lumasamples and above-right adjacent luma samples.
 3. The method of claim 1,wherein the first down-sampled filtering scheme calculates a specificdown-sampled above neighboring luma sample D from selected luma samplesof the only one above luma sample row as:D=(a[2i−1]+2*a[2i]+a[2i+1]+2)>>2, wherein a[2i−1], a[2i] and a[2i+1]represent the selected luma samples.
 4. The method of claim 3, whereinin a case that one of the selected luma samples is unavailable, applyinga padding process in which the unavailable luma sample is assigned withanother available luma sample.
 5. The method of claim 4, wherein in acase that a[2i−1] is unavailable, the specific down-sampled aboveneighboring luma sample D is derived as:D=(3*a[2i]+a[2i+1]+2)>>2.
 6. The method of claim 1, wherein multipleabove luma sample rows and a filter with taps more than 3 are used togenerate the down-sampled above neighboring luma samples in the seconddown-sampled filtering scheme.
 7. The method of claim 6, wherein atleast one of multiple above luma sample rows is not adjacent to thecorresponding block.
 8. The method of claim 1, further comprising:applying a third down-sampled filtering scheme to generate thedown-sampled inside luma samples of the corresponding luma block,wherein the third down-sampled filtering scheme uses same filtering tapsand filtering coefficients with the second down-sampled filteringscheme.
 9. The method of claim 1, further comprising: applying a fourthdown-sampled filtering scheme to generate down-sampled left neighboringluma samples of the corresponding luma block, wherein the fourthdown-sampled filtering scheme uses same filtering taps and filteringcoefficients with the second down-sampled filtering scheme; and theparameters of the cross-component linear model are further derived basedon the down-sampled left neighboring luma samples.
 10. The method ofclaim 1, wherein the first down-sampled filtering scheme corresponds todown-sampling above neighboring luma samples to lower left positions.11. The method of claim 1, wherein the conversion includes encoding thecurrent video block into the bitstream.
 12. The method of claim 1,wherein the conversion includes decoding the current video block fromthe bitstream.
 13. An apparatus for processing video data comprising aprocessor and a non-transitory memory with instructions thereon, whereinthe instructions upon execution by the processor, cause the processorto: determine, for a conversion between a current video block of a videothat is a chroma block and a bitstream of the video, a correspondingluma block for the current video block, wherein a color format for thecurrent video block and the corresponding luma block is 4:2:0, and across component linear mode is applied for the current video block;generate down-sampled inside luma samples of the corresponding lumablock; generate down-sampled above neighboring luma samples of thecorresponding luma block, wherein different down-sampled filteringschemes are used to generate the down-sampled above neighboring lumasamples based on a position of the corresponding luma block, and whereinin response to the position of the corresponding luma block meeting afirst position rule, a first down-sampled filtering scheme is used togenerate the down-sampled above neighboring luma samples, and whereinthe first position rule is that a top boundary of the corresponding lumablock is overlapping with a top boundary of a current luma coding treeblock (CTB) including the corresponding luma block, and wherein only oneabove luma sample row and a horizontal 3-tap filter are used to generatethe down-sampled above neighboring luma samples in the firstdown-sampled filtering scheme; derive parameters of the cross-componentlinear model at least based on the down-sampled above neighboring lumasamples; generate predicted chroma samples of the current video blockbased on the parameters of the cross-component linear model and thedown-sampled inside luma samples; and perform the conversion based onthe predicted chroma samples, wherein the only one above luma sample rowis adjacent to the corresponding luma block; and wherein in response tothe position of the corresponding luma block not meeting the firstposition rule, a second down-sampled filtering scheme is used togenerate the down-sampled above neighboring luma samples.
 14. Theapparatus of claim 13, wherein the only one above luma sample rowcomprise the above neighboring luma samples and above-right neighboringluma samples.
 15. The apparatus of claim 13, wherein the only one aboveluma sample row comprises above adjacent luma samples and above-rightadjacent luma samples.
 16. The apparatus of claim 13, wherein the firstdown-sampled filtering scheme calculates a specific down-sampled aboveneighboring luma sample D from selected luma samples of the only oneabove luma sample row as:D=(a[2i−1]+2*a[2i]+a[2i+1]+2)>>2, wherein a[2i−1], a[2i] and a[2i+1]represent the selected luma samples.
 17. The apparatus of claim 16,wherein in a case that one of the selected luma samples is unavailable,applying a padding process in which the unavailable luma sample isassigned with another available luma sample.
 18. A non-transitorycomputer-readable storage medium storing instructions that cause aprocessor to: determine, for a conversion between a current video blockof a video that is a chroma block and a bitstream of the video, acorresponding luma block for the current video block, wherein a colorformat for the current video block and the corresponding luma block is4:2:0, and a cross component linear mode is applied for the currentvideo block; generate down-sampled inside luma samples of thecorresponding luma block; generate down-sampled above neighboring lumasamples of the corresponding luma block, wherein different down-sampledfiltering schemes are used to generate the down-sampled aboveneighboring luma samples based on a position of the corresponding lumablock, and wherein in response to the position of the corresponding lumablock meeting a first position rule, a first down-sampled filteringscheme is used to generate the down-sampled above neighboring lumasamples, and wherein the first position rule is that a top boundary ofthe corresponding luma block is overlapping with a top boundary of acurrent luma coding tree block (CTB) including the corresponding lumablock, and wherein only one above luma sample row and a horizontal 3-tapfilter are used to generate the down-sampled above neighboring lumasamples in the first down-sampled filtering scheme; derive parameters ofthe cross-component linear model at least based on the down-sampledabove neighboring luma samples; generate predicted chroma samples of thecurrent video block based on the parameters of the cross-componentlinear model and the down-sampled inside luma samples; and perform theconversion based on the predicted chroma samples, wherein the only oneabove luma sample row is adjacent to the corresponding luma block; andwherein in response to the position of the corresponding luma block notmeeting the first position rule, a second down-sampled filtering schemeis used to generate the down-sampled above neighboring luma samples. 19.The non-transitory computer-readable storage medium of claim 18, whereinthe only one above luma sample row comprise the above neighboring lumasamples and above-right neighboring luma samples.
 20. A non-transitorycomputer-readable recording medium storing a bitstream which isgenerated by a method performed by a video processing apparatus, whereinthe method comprises: determining, for a conversion between a currentvideo block of a video that is a chroma block and the bitstream of thevideo, a corresponding luma block for the current video block, wherein acolor format for the current video block and the corresponding lumablock is 4:2:0, and a cross component linear mode is applied for thecurrent video block; generating down-sampled inside luma samples of thecorresponding luma block; generating down-sampled above neighboring lumasamples of the corresponding luma block, wherein different down-sampledfiltering schemes are used to generate the down-sampled aboveneighboring luma samples based on a position of the corresponding lumablock, and wherein in response to the position of the corresponding lumablock meeting a first position rule, a first down-sampled filteringscheme is used to generate the down-sampled above neighboring lumasamples, wherein the first position rule is that a top boundary of thecorresponding luma block is overlapping with a top boundary of a currentluma coding tree block (CTB) including the corresponding luma block, andwherein only one above luma sample row and a horizontal 3-tap filter areused to generate the down-sampled above neighboring luma samples in thefirst down-sampled filtering scheme; deriving parameters of thecross-component linear model at least based on the down-sampled aboveneighboring luma samples; generating predicted chroma samples of thecurrent video block based on the parameters of the cross-componentlinear model and the down-sampled inside luma samples; and generatingthe bitstream based on the predicted chroma samples, wherein the onlyone above luma sample row is adjacent to the corresponding luma block;and wherein in response to the position of the corresponding luma blocknot meeting the first position rule, a second down-sampled filteringscheme is used to generate the down-sampled above neighboring lumasamples.